OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HAND WRITTEN MIXED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER
Optical Character recognition is the method of digitalization of hand and type written or
printed text into machine-encoded form and is superfluity of the various applications of envision
of human’s life. In present human life OCR has been successfully using in finance, legal,
banking, health care and home need appliances. India is a multi cultural, literature and
traditional scripted country. Telugu is the southern Indian language, it is a syllabic language,
symbol script represents a complete syllable and formed with the conjunct mixed consonants in
their representation. Recognition of mixed conjunct consonants is critical than the normal
consonants, because of their variation in written strokes, conjunct maxing with pre and post
level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient
classification of the hand written and printed conjunct consonants. This paper implements the
Advanced Fuzzy Logic system controller to take the text in the form of written or printed,
collected the text images from the scanned file, digital camera, Processing the Image with
Examine the high intensity of images based on the quality ration, Extract the image characters
depends on the quality then check the character orientation and alignment then to check the
character thickness, base and print ration. The input image characters can classify into the two
ways, first way represents the normal consonants and the second way represents conjunct
consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here
recognition process starts from middle layer, and then it continues to check the top and bottom
layers. The recognition process treat as conjunct consonants when it can detect any symbolic
characters in top and bottom layers of present base character otherwise treats as normal
consonants. The post processing technique applied to all three layered characters. Post
processing of the image: concentrated on the image text readability and compatibility, if the
readability is not process then repeat the process again. In this recognition process includes
slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character
recognition and post-processing modules were discussed. The main objectives to the
development of this paper are: To develop the classification, identification of deference
prototyping for written and printed consonants, conjunct consonants and symbols based on 3
layered approaches with different measurable area by using fuzzy logic and to determine
suitable features for handwritten character recognition.
OCR-THE 3 LAYERED APPROACH FOR DECISION MAKING STATE AND IDENTIFICATION OF TE...ijaia
Optical Character recognition is the method of digitalization of hand and type written or printed text into
machine-encoded form and is superfluity of the various applications of envision of human’s life. In present
human life OCR has been successfully using in finance, legal, banking, health care and home need
appliances. India is a multi cultural, literature and traditional scripted country. Telugu is the southern
Indian language, it is a syllabic language, symbol script represents a complete syllable and formed with the
conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical
than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and
post level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient classification of
the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system
controller to take the text in the form of written or printed, collected the text images from the scanned file,
digital camera, Processing the Image with Examine the high intensity of images based on the quality
ration, Extract the image characters depends on the quality then check the character orientation and
alignment then to check the character thickness, base and print ration. The input image characters can
classify into the two ways, first way represents the normal consonants and the second way represents
conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process
starts from middle layer, and then it continues to check the top and bottom layers. The recognition process
treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of
present base character otherwise treats as normal consonants. The post processing technique applied to all
three layered characters. Post processing of the image: concentrated on the image text readability and
compatibility, if the readability is not process then repeat the process again. In this recognition process
includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character recognition and
post-processing modules were discussed. The main objectives to the development of this paper are: To
develop the classification, identification of deference prototyping for written and printed consonants,
conjunct consonants and symbols based on 3 layered approaches with different measurable area by using
fuzzy logic and to determine suitable features for handwritten character recognition.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Handwritten character recognition is one of the most challenging and ongoing areas of research in the
field of pattern recognition. HCR research is matured for foreign languages like Chinese and Japanese but
the problem is much more complex for Indian languages. The problem becomes even more complicated for
South Indian languages due to its large character set and the presence of vowels modifiers and compound
characters. This paper provides an overview of important contributions and advances in offline as well as
online handwritten character recognition of Malayalam scripts.
Handwriting Recognition Using Deep Learning and Computer VersionNaiyan Noor
This document presents a method for handwriting recognition using deep learning and computer vision. It discusses preprocessing images by removing noise and converting to grayscale. Thresholding is used to separate darker text pixels from lighter background pixels. The image is then segmented into individual lines and words. Python libraries like TensorFlow, Spyder and Jupyter Notebook are used. The goal is to build a system that can recognize text in images and display the text to users. Future work may include recognizing cursive text and additional languages.
On-line handwriting recognition involves converting handwriting as it is written on a digitizer to digital text, while off-line recognition converts static images of handwriting. Both techniques face challenges from variability in handwriting styles. Current methods use feature extraction and neural networks, but do not match human-level recognition abilities. Handwriting recognition remains an important but difficult area of research.
Artificial Neural Network For Recognition Of Handwritten Devanagari CharacterIOSR Journals
1) The document discusses recognizing handwritten Devanagari characters using artificial neural networks and zone-based feature extraction.
2) It proposes extracting features from images by dividing them into zones and calculating average pixel distances to the image and zone centroids.
3) This zone-based feature vector is then input to a feedforward neural network for character recognition.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
OCR-THE 3 LAYERED APPROACH FOR DECISION MAKING STATE AND IDENTIFICATION OF TE...ijaia
Optical Character recognition is the method of digitalization of hand and type written or printed text into
machine-encoded form and is superfluity of the various applications of envision of human’s life. In present
human life OCR has been successfully using in finance, legal, banking, health care and home need
appliances. India is a multi cultural, literature and traditional scripted country. Telugu is the southern
Indian language, it is a syllabic language, symbol script represents a complete syllable and formed with the
conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical
than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and
post level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient classification of
the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system
controller to take the text in the form of written or printed, collected the text images from the scanned file,
digital camera, Processing the Image with Examine the high intensity of images based on the quality
ration, Extract the image characters depends on the quality then check the character orientation and
alignment then to check the character thickness, base and print ration. The input image characters can
classify into the two ways, first way represents the normal consonants and the second way represents
conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process
starts from middle layer, and then it continues to check the top and bottom layers. The recognition process
treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of
present base character otherwise treats as normal consonants. The post processing technique applied to all
three layered characters. Post processing of the image: concentrated on the image text readability and
compatibility, if the readability is not process then repeat the process again. In this recognition process
includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character recognition and
post-processing modules were discussed. The main objectives to the development of this paper are: To
develop the classification, identification of deference prototyping for written and printed consonants,
conjunct consonants and symbols based on 3 layered approaches with different measurable area by using
fuzzy logic and to determine suitable features for handwritten character recognition.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Handwritten character recognition is one of the most challenging and ongoing areas of research in the
field of pattern recognition. HCR research is matured for foreign languages like Chinese and Japanese but
the problem is much more complex for Indian languages. The problem becomes even more complicated for
South Indian languages due to its large character set and the presence of vowels modifiers and compound
characters. This paper provides an overview of important contributions and advances in offline as well as
online handwritten character recognition of Malayalam scripts.
Handwriting Recognition Using Deep Learning and Computer VersionNaiyan Noor
This document presents a method for handwriting recognition using deep learning and computer vision. It discusses preprocessing images by removing noise and converting to grayscale. Thresholding is used to separate darker text pixels from lighter background pixels. The image is then segmented into individual lines and words. Python libraries like TensorFlow, Spyder and Jupyter Notebook are used. The goal is to build a system that can recognize text in images and display the text to users. Future work may include recognizing cursive text and additional languages.
On-line handwriting recognition involves converting handwriting as it is written on a digitizer to digital text, while off-line recognition converts static images of handwriting. Both techniques face challenges from variability in handwriting styles. Current methods use feature extraction and neural networks, but do not match human-level recognition abilities. Handwriting recognition remains an important but difficult area of research.
Artificial Neural Network For Recognition Of Handwritten Devanagari CharacterIOSR Journals
1) The document discusses recognizing handwritten Devanagari characters using artificial neural networks and zone-based feature extraction.
2) It proposes extracting features from images by dividing them into zones and calculating average pixel distances to the image and zone centroids.
3) This zone-based feature vector is then input to a feedforward neural network for character recognition.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document describes a project using a neural network and MATLAB for handwritten character recognition. The goal is to train a neural network to classify individual handwritten characters. The solution approach involves preprocessing images to extract characters, extracting features from the characters, training the neural network, and creating a graphical user interface application. Image preprocessing includes converting to grayscale, thresholding to binary, connectivity testing, and cropping characters. Feature extraction calculates 17 attributes for each character like position, size, pixel counts and distributions. The neural network is then trained on this dataset to classify characters for the application.
This document is a project report submitted by Mohammad Saiful Islam for a CMPUT 551 course on December 21st, 2010 regarding Bengali handwritten digit recognition using support vector machines. The report discusses building a dataset of Bengali digits written by the author, preprocessing and feature extraction steps, and using a multiclass support vector machine with different kernels for classification. The author hypothesizes that SVM will perform well, RBF kernels will improve performance over linear and polynomial kernels, and using raw pixel values can achieve good accuracy, though testing on different writers may reduce performance. Experiments are planned to test these hypotheses using the collected dataset.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
The document describes an OCR system for recognition of Urdu text written in Nastaliq font. It discusses the characteristics of Urdu script, existing approaches for cursive script recognition, and the methodology used in the system. The system employs two holistic approaches - a multi-tier approach using neural networks and a multi-stage classification approach combining multiple classifiers. Results show the 20 most frequent ligatures identified from analyzing BBC Urdu news text, and feature vectors extracted for segmentation.
Rule based algorithm for handwritten characters recognitionRanda Elanwar
This presentation discusses document analysis and character recognition. It begins with an introduction that motivates DAR and CR research. It then describes the fields of off-line and on-line document analysis and CR. Key aspects covered include preprocessing, feature extraction, segmentation, learning and classification. The objective is to achieve high character recognition accuracy for isolated and cursive Arabic characters using rule-based algorithms. The presentation describes the database collection and a rule-based algorithm for isolated offline handwritten character recognition.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Online Hand Written Character RecognitionIOSR Journals
This document discusses online handwritten character recognition. It begins by describing the differences between online and offline recognition systems. Online systems capture stroke order and timing information while writing, while offline systems analyze static images. The document then discusses challenges in recognition like variability between writers. It presents several previous works in online handwriting recognition. The document proposes a method for online recognition that uses shape, pixel density, and stroke movement template matching to identify characters. It describes preprocessing input and generating training templates to match against. Overall, the document outlines challenges in online handwriting recognition and proposes a template matching approach to address these challenges.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
The document discusses optical character recognition for Urdu handwriting. It introduces OCR and its applications. It then discusses earlier work on OCR systems that were font-specific. The document outlines the steps in OCR including image acquisition, preprocessing, segmentation, feature extraction, classification, and recognition. It provides an overview of the Urdu script and its variations. The document then summarizes research conducted on recognizing offline isolated Urdu characters using moment invariants and support vector machines. Other works discussed include an online and offline OCR system for Urdu using a segmentation-free approach, classifying Urdu ligatures using convolutional neural networks, and a segmentation-based approach for Urdu Nastaliq script recognition.
This document discusses text detection and character recognition from images. It begins with an introduction and then discusses the aims, objectives, motivation and problem statement. It reviews relevant literature on segmentation and recognition techniques. The document then describes the methodology used, including preprocessing, segmentation using vertical projections and connected components, and recognition using pixel counting, projections, template matching, Fourier descriptors and heuristic filters. It presents results from four experiments comparing different segmentation and recognition methods. The discussion analyzes results and limitations. The conclusion finds that segmentation works best with connected components while recognition works best with template matching, Fourier descriptors and heuristic filters.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
Mixed Language Based Offline Handwritten Character Recognition Using First St...CSCJournals
Artificial Neural Network is an artificial representation of the human brain that tries to simulate its learning process. To train a network and measure how well it performs, an objective function must be defined. A commonly used performance criterion function is the sum of squares error function. Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. Language identification and interpretation of handwritten characters is one of the challenges faced in various industries. For example, it is always a big challenge in data interpretation from cheques in banks, language identification and translated messages from ancient script in the form of manuscripts, palm scripts and stone carvings to name a few. Handwritten character recognition using Soft computing methods like Neural networks is always a big area of research for long time and there are multiple theories and algorithms developed in the area of neural networks for handwritten character recognition.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques for English, Indian languages, license plate recognition, document binarization, and removing "bleed-through" effects from financial documents.
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering csandit
Computerized medical image segmentation is a challenging area because of poor resolution
and weak contrast. The predominantly used conventional clustering techniques and the
thresholding methods suffer from limitations owing to their heavy dependence on user
interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The
performance further deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering
for segmenting vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for abnormality
detection and thus proper localisation and labelling will enhance the diagnostic output of a
physician. The method is compared with Otsu thresholding and K-means clustering to illustrate
the robustness. The reference standard for validation was the annotated images from the
radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
A Cross Layer Based Scalable Channel Slot Re-Utilization Technique for Wirele...csandit
Due to tremendous growth of the wireless based application services are increasing the demand
for wireless communication techniques that use bandwidth more effectively. Channel slot reutilization
in multi-radio wireless mesh networks is a very challenging problem. WMNs have
been adopted as back haul to connect various networks such as Wi-Fi (802.11), WI-MAX
(802.16e) etc. to the internet. The slot re-utilization technique proposed so far suffer due to high
collision due to improper channel slot usage approximation error. To overcome this here the
author propose the cross layer optimization technique by designing a device classification
based channel slot re-utilization routing strategy which considers the channel slot and node
information from various layers and use some of these parameters to approximate the risk
involve in channel slot re-utilization in order to improve the QoS of the network. The simulation
and analytical results show the effectiveness of our proposed approach in term of channel slot
re-utilization efficiency and thus helps in reducing latency for data transmission and reduce
channel slot collision.
This document describes a project using a neural network and MATLAB for handwritten character recognition. The goal is to train a neural network to classify individual handwritten characters. The solution approach involves preprocessing images to extract characters, extracting features from the characters, training the neural network, and creating a graphical user interface application. Image preprocessing includes converting to grayscale, thresholding to binary, connectivity testing, and cropping characters. Feature extraction calculates 17 attributes for each character like position, size, pixel counts and distributions. The neural network is then trained on this dataset to classify characters for the application.
This document is a project report submitted by Mohammad Saiful Islam for a CMPUT 551 course on December 21st, 2010 regarding Bengali handwritten digit recognition using support vector machines. The report discusses building a dataset of Bengali digits written by the author, preprocessing and feature extraction steps, and using a multiclass support vector machine with different kernels for classification. The author hypothesizes that SVM will perform well, RBF kernels will improve performance over linear and polynomial kernels, and using raw pixel values can achieve good accuracy, though testing on different writers may reduce performance. Experiments are planned to test these hypotheses using the collected dataset.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
The document describes an OCR system for recognition of Urdu text written in Nastaliq font. It discusses the characteristics of Urdu script, existing approaches for cursive script recognition, and the methodology used in the system. The system employs two holistic approaches - a multi-tier approach using neural networks and a multi-stage classification approach combining multiple classifiers. Results show the 20 most frequent ligatures identified from analyzing BBC Urdu news text, and feature vectors extracted for segmentation.
Rule based algorithm for handwritten characters recognitionRanda Elanwar
This presentation discusses document analysis and character recognition. It begins with an introduction that motivates DAR and CR research. It then describes the fields of off-line and on-line document analysis and CR. Key aspects covered include preprocessing, feature extraction, segmentation, learning and classification. The objective is to achieve high character recognition accuracy for isolated and cursive Arabic characters using rule-based algorithms. The presentation describes the database collection and a rule-based algorithm for isolated offline handwritten character recognition.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
CHARACTER RECOGNITION USING NEURAL NETWORK WITHOUT FEATURE EXTRACTION FOR KAN...Editor IJMTER
Handwriting recognition has been one of the active and challenging research areas in the
field of pattern recognition. It has numerous applications which include, reading aid for blind, bank
cheques and conversion of any hand written document into structural text form[1]. As there are no
sufficient number of works on Indian language character recognition especially Kannada script
among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten
Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized
into 60x40 pixel.The resized character is used for training the neural network. Once the training
process is completed the same character is given as input to the neural network with different set of
neurons in hidden layer and their recognition accuracy rate for different kannada characters has been
calculated and compared. The results show that the proposed system yields good recognition
accuracy rates comparable to that of other handwritten character recognition systems.
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Online Hand Written Character RecognitionIOSR Journals
This document discusses online handwritten character recognition. It begins by describing the differences between online and offline recognition systems. Online systems capture stroke order and timing information while writing, while offline systems analyze static images. The document then discusses challenges in recognition like variability between writers. It presents several previous works in online handwriting recognition. The document proposes a method for online recognition that uses shape, pixel density, and stroke movement template matching to identify characters. It describes preprocessing input and generating training templates to match against. Overall, the document outlines challenges in online handwriting recognition and proposes a template matching approach to address these challenges.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
The document discusses optical character recognition for Urdu handwriting. It introduces OCR and its applications. It then discusses earlier work on OCR systems that were font-specific. The document outlines the steps in OCR including image acquisition, preprocessing, segmentation, feature extraction, classification, and recognition. It provides an overview of the Urdu script and its variations. The document then summarizes research conducted on recognizing offline isolated Urdu characters using moment invariants and support vector machines. Other works discussed include an online and offline OCR system for Urdu using a segmentation-free approach, classifying Urdu ligatures using convolutional neural networks, and a segmentation-based approach for Urdu Nastaliq script recognition.
This document discusses text detection and character recognition from images. It begins with an introduction and then discusses the aims, objectives, motivation and problem statement. It reviews relevant literature on segmentation and recognition techniques. The document then describes the methodology used, including preprocessing, segmentation using vertical projections and connected components, and recognition using pixel counting, projections, template matching, Fourier descriptors and heuristic filters. It presents results from four experiments comparing different segmentation and recognition methods. The discussion analyzes results and limitations. The conclusion finds that segmentation works best with connected components while recognition works best with template matching, Fourier descriptors and heuristic filters.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
Mixed Language Based Offline Handwritten Character Recognition Using First St...CSCJournals
Artificial Neural Network is an artificial representation of the human brain that tries to simulate its learning process. To train a network and measure how well it performs, an objective function must be defined. A commonly used performance criterion function is the sum of squares error function. Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. Language identification and interpretation of handwritten characters is one of the challenges faced in various industries. For example, it is always a big challenge in data interpretation from cheques in banks, language identification and translated messages from ancient script in the form of manuscripts, palm scripts and stone carvings to name a few. Handwritten character recognition using Soft computing methods like Neural networks is always a big area of research for long time and there are multiple theories and algorithms developed in the area of neural networks for handwritten character recognition.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques for English, Indian languages, license plate recognition, document binarization, and removing "bleed-through" effects from financial documents.
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering csandit
Computerized medical image segmentation is a challenging area because of poor resolution
and weak contrast. The predominantly used conventional clustering techniques and the
thresholding methods suffer from limitations owing to their heavy dependence on user
interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The
performance further deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering
for segmenting vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for abnormality
detection and thus proper localisation and labelling will enhance the diagnostic output of a
physician. The method is compared with Otsu thresholding and K-means clustering to illustrate
the robustness. The reference standard for validation was the annotated images from the
radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
A Cross Layer Based Scalable Channel Slot Re-Utilization Technique for Wirele...csandit
Due to tremendous growth of the wireless based application services are increasing the demand
for wireless communication techniques that use bandwidth more effectively. Channel slot reutilization
in multi-radio wireless mesh networks is a very challenging problem. WMNs have
been adopted as back haul to connect various networks such as Wi-Fi (802.11), WI-MAX
(802.16e) etc. to the internet. The slot re-utilization technique proposed so far suffer due to high
collision due to improper channel slot usage approximation error. To overcome this here the
author propose the cross layer optimization technique by designing a device classification
based channel slot re-utilization routing strategy which considers the channel slot and node
information from various layers and use some of these parameters to approximate the risk
involve in channel slot re-utilization in order to improve the QoS of the network. The simulation
and analytical results show the effectiveness of our proposed approach in term of channel slot
re-utilization efficiency and thus helps in reducing latency for data transmission and reduce
channel slot collision.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
COQUEL: A CONCEPTUAL QUERY LANGUAGE BASED ON THE ENTITYRELATIONSHIP MODELcsandit
As more and more collections of data are available on the Internet, end users but not experts in
Computer Science demand easy solutions for retrieving data from these collections. A good
solution for these users is the conceptual query languages, which facilitate the composition of
queries by means of a graphical interface. In this paper, we present (1) CoQueL, a conceptual
query language specified on E/R models and (2) a translation architecture for translating
CoQueL queries into languages such as XQuery or SQL..
FILESHADER: ENTRUSTED DATA INTEGRATION USING HASH SERVER csandit
This document summarizes a research paper that proposes FileShader, a system using hash values to ensure file integrity during transfers between clients and servers. FileShader works by having the file provider calculate and send the hash value of a file to a trusted hash server. When clients download a file, FileShader calculates the hash and compares it to the value stored on the hash server to detect any changes. The researchers implemented a prototype of FileShader and found it could accurately detect file changes with little performance overhead. They conclude FileShader is a practical solution that can increase security for internet users by verifying file integrity during transfers.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
Associative Regressive Decision Rule Mining for Predicting Customer Satisfact...csandit
Opinion mining also known as sentiment analysis, involves customer satisfactory patterns,
sentiments and attitudes toward entities, products, services and their attributes. With the rapid
development in the field of Internet, potential customer’s provides a satisfactory level of
product/service reviews. The high volume of customer reviews were developed for
product/review through taxonomy-aware processing but, it was difficult to identify the best
reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is
developed to predict the pattern for service provider and to improve customer satisfaction based
on the review comments. Associative Regression based Decision Rule Mining performs twosteps
for improving the customer satisfactory level. Initially, the Machine Learning Bayes
Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After
that, Regressive factor of the opinion words and Class labels were checked for Association
between the words by using various probabilistic rules. Based on the probabilistic rules, the
opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of
service preferred by the customers with their review comments. The Associative Regressive
Decision Rule helps the service provider to take decision on improving the customer satisfactory
level. The experimental results reveal that the Associative Regression Decision Rule Mining
(ARDRM) technique improved the performance in terms of true positive rate, Associative
Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of
similar pattern.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcsandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
Mining Fuzzy Association Rules from Web Usage Quantitative Data csandit
Web usage mining is the method of extracting interesting patterns from Web usage log file. Web
usage mining is subfield of data mining uses various data mining techniques to produce
association rules. Data mining techniques are used to generate association rules from
transaction data. Most of the time transactions are boolean transactions, whereas Web usage
data consists of quantitative values. To handle these real world quantitative data we used fuzzy
data mining algorithm for extraction of association rules from quantitative Web log file. To
generate fuzzy association rules first we designed membership function. This membership
function is used to transform quantitative values into fuzzy terms. Experiments are carried out
on different support and confidence. Experimental results show the performance of the
algorithm with varied supports and confidence.
GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
A Routing Protocol Orphan-Leach to Join Orphan Nodes in Wireless Sensor Netwo...csandit
The hierarchical routing protocol LEACH (Low Energy Adaptive Clustering Hierarchy) is
referred to as the basic algorithm of distributed clustering protocols. LEACH allows clusters
formation. Each cluster has a leader called Cluster Head (CH). The selection of CHs is made
with a probabilistic calculation. It is supposed that each non-CH node join a cluster and
becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the
network. Thus several sensor nodes cannot reach any CH. As a result, the remaining part of the
controlled field will not be covered; some sensor nodes will be outside the network. To solve this
problem, we propose O-LEACH (Orphan Low Energy Adaptive Clustering Hierarchy) a routing
protocol that takes into account the orphan nodes. Indeed, a cluster member will be able to play
the role of a gateway which allows the joining of orphan nodes. If a gateway node has to
connect a important number of orphan nodes, thus a sub-cluster is created and the gateway
node is considered as a CH’ for connected orphans. As a result, orphan nodes become able to
send their data messages to the CH which performs in turn data aggregation and send
aggregated data message to the CH. The WSN application receives data from the entire network
including orphan nodes.
The simulation results show that O-LEACH performs better than LEACH in terms of
connectivity rate, energy, scalability and coverage.
This document provides an overview of fuzzy logic and fuzzy sets. It discusses key concepts such as membership functions, operations on fuzzy sets like complement, intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. The document also introduces fuzzy rules and compares classical rules to fuzzy rules. Finally, it provides examples of applying concepts like support, core and complement to fuzzy sets.
Fuzzy Transfer Learning for Intelligent Environmentsjethroshell
The dynamic nature of Intelligent Environments (IE’s) present a challenging problem when attempting to model or learn a model of such environments. By their very nature, IE’s are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. As a result of this, the quantity, type and availability of data to model such environments is a major issue. Each situation is contextually different and constantly changing. To model each application, training data must be gained that is within the same feature space and has the same distribution, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of target labelled data occurs. It is within these situations that our study is focussed. Within this research we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a Fuzzy Transfer Learning process.
This presentation is taken from the winning entry at the Leicester British Computer Society Postgraduate Research Competition 2012.
BrailleOCR: An Open Source Document to Braille Converter Applicationpijush15
This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired.
What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here
IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254
Project site: https://code.google.com/p/brailleocr/
The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.
The document summarizes key concepts from Chapter 10 of Bishop's PRML book on approximate inference using variational methods. It introduces variational inference as a deterministic alternative to importance sampling for approximating intractable distributions. Variational inference frames inference as an optimization problem of variationally approximating the true posterior using a simpler distribution from an assumed family. This is done by maximizing a lower bound on the marginal likelihood. Mean-field variational inference further assumes a factorized form for the variational distribution.
Fuzzy logic is a form of logic that accounts for partial truth and vagueness. It is used in applications like ABS brakes, control units, and air conditioners. A fuzzy logic system defines membership functions that assign a degree of truth between 0 and 1 to inputs and uses a rules and inference mechanism to map inputs to outputs. For example, a fuzzy air conditioner controller defines membership functions for temperature inputs like "cold" and outputs like "stop" or "fast", and uses rules like "if cold then stop" to determine the fan speed based on the temperature.
This document provides an overview of the Tesseract OCR engine, including a brief history of OCR technology, details about Tesseract's development and architecture, and announcements about version 2.00. It describes how Tesseract uses techniques like text line finding, baseline approximation, and static/adaptive classifiers to recognize words despite challenges with spacing, italics, and other font variations. The document also notes areas where commercial OCR systems may have advantages over Tesseract, such as page layout analysis, language support, and accuracy.
A Novel Approach for Breast Cancer Detection using Data Mining Techniquesahmad abdelhafeez
The document presents an agenda for a talk on using data mining techniques for breast cancer detection. It begins with background on cancer and breast cancer. It then discusses pattern recognition, data mining tools like Weka, and various classification techniques including Naive Bayes, SVM, C4.5, KNN, BF tree, and IBK that could be used. The talk will cover preprocessing data, feature selection, applying classification algorithms, and evaluating performance. The goal is to introduce a novel approach for breast cancer detection using these data mining methods.
APPLICATION OF FUZZY LOGIC TO VISUAL EXAMINATION IN THE ASSESSMENT OF SULPHAT...ijfls
This document describes a study that uses fuzzy logic to assess the confidence that can be placed in visual examination results for evaluating sulphate attack on cement-based materials. The study considers three input parameters - the type of cation in sulphate solutions, the quality of concrete, and quality of visual observation. It defines membership functions to represent each parameter and develops rules to relate the inputs to an output of confidence level. The fuzzy inference system is applied to various combinations of input parameter values to determine the resulting confidence output. The results show that high confidence in the output relies on high confidence values for all three input parameters. Varying the individual input parameters demonstrates the effect of uncertainty in each on the resulting confidence level.
APPLICATION OF FUZZY LOGIC TO VISUAL EXAMINATION IN THE ASSESSMENT OF SULPHAT...
Similar to OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HAND WRITTEN MIXED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
Text detection and recognition in scene images or natural images has applications in computer
vision systems like registration number plate detection, automatic traffic sign detection, image retrieval
and help for visually impaired people. Scene text, however, has complicated background, blur image,
partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text
recognition could be a difficult computer vision problem. In this paper connected component method
is used to extract the text from background. In this work, horizontal and vertical projection profiles,
geometric properties of text, image binirization and gap filling method are used to extract the text from
scene images. Then histogram based threshold is applied to separate text background of the images.
Finally text is extracted from images.
FREEMAN CODE BASED ONLINE HANDWRITTEN CHARACTER RECOGNITION FOR MALAYALAM USI...acijjournal
Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm.
Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Character Recognition (Devanagari Script)IJERA Editor
This document summarizes research on using neural networks for optical character recognition of Devanagari script characters. It describes preprocessing scanned images, extracting features using neural networks, and post-processing to recognize characters. The system was tested on a dataset of Devanagari characters with neural networks trained over multiple epochs. Recognition accuracy increased with larger training sets as the network learned to identify characters more precisely. The system demonstrates an effective approach for digitally recognizing handwritten Devanagari characters.
This document summarizes and reviews various techniques for optical character recognition (OCR) of English text, including matrix matching, fuzzy logic, feature extraction, structural analysis, and neural networks. It discusses the structure and stages of OCR systems, including image preprocessing, segmentation, feature extraction, classification, and output. Challenges for OCR systems include degraded documents like old books, photocopies, and newspapers. The document reviews several related works on OCR and discusses techniques to improve recognition of degraded text.
1) The document discusses recognizing handwritten Devanagari characters using an artificial neural network approach.
2) It proposes extracting features from images by dividing them into zones and calculating average pixel distances to the image and zone centroids.
3) This zone-based feature extraction method is used to create a feature vector for each character image that is then classified by a feedforward neural network.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
This document summarizes research on recognizing handwritten characters in the Odia language. It discusses two main approaches to Odia character recognition: template matching and feature extraction. The document also reviews several papers on Odia handwritten character recognition, describing the different techniques used, such as neural networks, genetic algorithms, and rule-based methods. Overall, the document surveys existing work on developing systems for Odia optical character recognition (OCR) and handwritten character recognition.
A Survey of Modern Character Recognition Techniquesijsrd.com
This document summarizes several modern techniques for handwritten character recognition. It discusses common feature extraction methods like statistical, structural and global transformation features. It then summarizes several papers that have proposed different techniques for handwritten character recognition, including using associative memory nets, moment invariants with support vector machines, neural networks, hidden markov models, gradient features, and multi-scale neural networks. The document concludes that neural networks are commonly used for training, and that feature extraction methods continue to be improved, but handwritten character recognition remains an active area of research.
This document discusses a handwriting recognition system that uses 3D discrete wavelet and multiwavelet transforms for feature extraction from Latin handwritten text. The proposed system performs preprocessing, feature extraction using 3D-DWT and 3D-DMWTCS transforms, pattern matching and classification using a minimum distance classifier, and postprocessing. The system achieves classification accuracies of 95.76% using 3D-DMWTCS and 94.05% using 3D-DWT on the Rimes database. The document provides background on handwriting recognition, typical models, and feature extraction methods including global, geometrical, topological, and statistical features.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
1. The document discusses an optical character recognition (OCR) system that uses a neural network to recognize handwritten English characters and numerals.
2. It describes the background of OCR, including offline vs online recognition. The key steps of OCR systems are discussed as image acquisition, preprocessing, feature extraction, training and recognition, and post processing.
3. Neural networks are described as being useful for pattern recognition problems like character classification. The proposed system uses a grid infrastructure to allow multi-lingual OCR and more efficient document processing compared to other methods.
Automatic signature verification with chain code using weighted distance and ...eSAT Journals
Abstract The signature forgery can be restricted by either online or offline signature verification techniques. It verifies the signature by
performing a match with the pre-processed signature dynamically by detecting the motion of stylus during signature while on
other hand, offline verifies by performing a match using the two dimensional scanned image of the signature. This paper studies
about the various techniques available in offline signature verification along with their shadows.
Keywords: Signature Verification, Weighted Distance, High Pressure Factor, Normalization, Threshold Value
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides a review of existing methods for handwritten character recognition based on geometrical properties. It begins by classifying character recognition as either printed or handwritten, and describes the different phases a character recognition system typically includes: image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing steps like binarization, noise removal, normalization and morphological operations are discussed. Feature extraction methods focused on include statistical, global and structural features. Geometrical features involving lines, loops, strokes and their directions are highlighted. Classification algorithms mentioned are neural networks, SVM, k-nearest neighbor, and genetic algorithms. The literature review provides examples of character recognition research using geometrical features like horizontal/vertical line analysis and directional feature
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Cursive Handwriting Recognition System using Feature Extraction and Artif...IRJET Journal
The document describes a system for recognizing cursive handwriting using feature extraction and an artificial neural network. It involves preprocessing scanned images, segmenting them into individual characters, extracting features from the characters using a diagonal scanning method, and classifying the characters using a neural network. This approach provides higher recognition accuracy compared to conventional methods. The key steps are preprocessing images, segmenting into characters, extracting 54 features from each character by moving along diagonals in a grid, and training a neural network classifier on the extracted features.
Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. Handwritten Character Recognition is one of the active and challenging research areas in the field of Pattern Recognition. Pattern recognition is a process that taking in raw data and making an action based on the category of the pattern. HCR is one of the well-known applications of pattern recognition. Handwriting recognition especially for Indian languages is still in infant stage because not much work has been done it. This paper discuss about an idea to recognize Kannada vowels using chain code features. Kannada is a South Indian language. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a chain code based feature extraction method is investigated for developing HCR system. Chain code is working based on 4-neighborhood or 8–neighborhood methods. Chain code is a sequence of code directions of a character and connection to a starting point which is often used in image processing. In this paper, 8–neighborhood method has been implemented which allows generation of eight different codes for each character. These codes have been used as features of the character image, which have been later on used for training and testing for K-Nearest Neighbor (KNN) classifiers. The level of accuracy reached to 100%.
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
This document describes two methods for recognizing handwritten Farsi characters using neural networks and machine learning techniques. The first method uses wavelet transforms to extract features from character borders and trains a neural network classifier on these features. It achieves 86.3% accuracy on test data. The second method divides characters into groups based on visual properties, extracts moment features for each group, and uses Bayesian classification with a decision tree post-processing step. It achieves an overall recognition rate of 90.64% according to the results presented. Experimental evaluations of both methods on different datasets of handwritten Farsi characters are discussed.
Similar to OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HAND WRITTEN MIXED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER (20)
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
How to Get CNIC Information System with Paksim Ga.pptx
OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HAND WRITTEN MIXED CONSONANTS AND CONJUNCT CONSONANTS BY USING ADVANCED FUZZY LOGIC CONTROLLER
2. 76 Computer Science & Information Technology (CS & IT)
KEYWORDS
Optical Character Recognition (OCR), TopMiddleBottom Layer (TMBL) layer,
BottomMiddleTop Layer (BMTL), Hand written and printed character recognition (HWPCR),
Artificial Neural Network (ANN).
1. INTRODUCTION
Optical Character Recognition is the method of digitalization of hand and type written or printed
text into machine-encoded form. OCR is the most active invention research area in the field of
image processing, character and pattern recognition. In present life OCR has been successfully
using in finance, legal, banking, health care and home need appliances. Character Recognition
classified into two ways, online and offline. Online character and pattern recognition method is
finer to their off mode counterparts in recognition of hand written characters due to the temporal
information available with the formal information. In Off-Line mode character recognition, the
written or printed document can be scanned as an image then it can be digitized then converted it
into machine readable form with different character reorganization algorithmic methodology.
Off-Line mode character recognition process is an active and effective research area towards to
development of new innovations, ideas and techniques that would improve recognition accuracy.
The OCR consists the different levels of processing methods like as Image Pre Acquisition,
Acquisition, Pre-processing, Segmentation, Post processing, Feature Extraction and
Classification. India is a multi cultural, literature and traditional scripted country. 18 official
scripted languages are formed and have many local regional languages in India. Telugu is the
official language of the southern Indian states of Telangana and Andhra Pradesh. Telugu is also
spoken in all over in Malaysia, Bahrain, Oman, Singapore, Fiji, UAE and Mauritius. Officially,
there are 10 numerals, 18 vowels, 36 consonants, and three dual symbols. Telugu is the Dravidian
composed language and it is the third most popular script in India. The Telugu script is closely
related to the Kannada script. In OCR, captured or scanned input image is active from number of
stages like Image Acquisition, pre-processing, processing, post-processing, segmentation, feature
extraction and classification to perform Optical Character Recognition. Scanned Images or
captured photographs taken as input for the OCR system in Image Acquisition stage. Pre-
processing is important and necessary to convert the raw data to correct deficiencies in the data
acquisition process due to limitations of the capturing device sensor. Pre-processing stage step
involves detect text stroke rate, binarization, normalization, noise removal and so on.
Segmentation is the process of dividing the individual cum grouped characters, separating line
spaces, words and mixed characters from scanned image. Feature extraction explores the exact
identification of the characters, can be considered as finding a set of features that define the shape
of the underlying character as precisely and uniquely as possible.
2. EARLIER WORK WITH OPTICAL CHARACTER RECOGNITION
ALGORITHMS
In the past and present invasion of OCR, many algorithms are designed for different ways of
character recognition processes such as Template Matching, Statistical Algorithm, Structural
Algorithm, Neural Network Algorithm and Support Vector Machine. Template Matching
Algorithm proposed only for the recognition of the typewritten characters. The Statistical
Algorithm, Structural Algorithm, Neural Network Algorithm and Support Vector Machine
proposed for recognition of both type and handwritten characters. Each algorithmic methodology
carries both advantages and disadvantages.
3. Computer Science & Information Technology (CS & IT) 77
2.1 Neural Network Algorithm
An Artificial Neural Network is an innovative methodology for information processing. ANN is
inspired by the biological nervous system, such as the main system act like as brain and its inter-
connected nerve process data. ANN composes huge number of inter-connected neurons for
processing data cum elements working in harmony to solve the problems [25]. A neural network
is a powerful data modelling tool that is able to capture and represent complex input/output
relationships. Neural network algorithm activated and identifies the characters by boosting and
worm-up of trained neuron of the neural network. Feed forward Neural Network, Feedback
neural network and Self Organizing Map are the types of neural network. Neural network
algorithm especially works for new characters can be found when it can middle of recognition
process, and also it is a suitable.
2.2 Support Vector Machine
The Support Vector Machine (SVM) is a related to support vector networks and set of supervised
learning methods used for classification. Support vector machine algorithm activated and
discover the characters by scrutinise and mapping the given input information on with high
priority dimensional future apace and it can be determine a dividing hyper plane with maximum
and minimum margin data. SVM is robust, accurate and very effective even though when the
training samples and models are less and it can perform good result without adding prior data sets
and feed information.
2.3 Structural Algorithm
The initial idea behind the creation of structural algorithms is the recursive description of a
complex pattern in terms of simpler patterns based on the size and shape of the object [23]. This
structural algorithm activated and identifies by recognize compound component of the character.
Structural algorithm classifies the input patterns on the basis of components of the characters and
the relationship among these components. Firstly the primitives of the character are identified
and then strings of the primitives are checked on the basis of pre-decided rules [00]. Structural
pattern recognition is intuitively appealing because in addition to classification, this approach also
provides a description of how the given path constructed from the primitives: [24]. Generally a
character is represented as a production rules structure, whose left-hand side represents character
labels and whose right-hand side represents string of primitives. The right-hand side of rules is
compared to the string of primitives extracted from a word. So classifying a character means
finding a path to a leaf: [22]. This algorithm mainly uses the structural shape pattern of the
objects.
2.4 Statistical Algorithm
The purpose of the Statistical Algorithm is to determine and categorize the given pattern based on
the statistical approach like as pre planned made observations, measurement approaches and a set
of numbers prepared which is used to prepare a measurement vector [22]. Statistical algorithm
uses the statistical decision functions and a set of optimality criteria which to maximizes the
probability of the observed pattern given the model of a certain class.
Statistical algorithms activated and identifies by making the measurement and assumptions.
Statistical algorithm is based on three assumptions. Such as distribution of present cum future set,
sufficient statistics presented in each class and collection of pre-images to extract a set of features
which represents each distinct class of image pattern. The major advantage is, it works even when
prior data or information is not available about the characters in the training data.
4. 78 Computer Science & Information Technology (CS & IT)
2.5 Template Matching Algorithm
Template Matching Algorithm known as pattern matching algorithm. All basic characters and
symbols are pre-stored in the system, and it is system prototype that useful to classify, identifies
the characters by comparing two pattern matching symbols or images. Template matching is the
process of finding the location of sub image called a template inside an image. Template
matching algorithm activated and identifies by Comparing derived image features and templates:
[21]. It is easy to implements but it only works on the pre-stored fonts and templates.
3. GENERAL POINTS OF CLASSIFICATION OF THE CONSONANTS AND
MIXED CONJUNCT CONSONANTS
General points might be concentrated during the process of handwritten character recognition
when digitized input image is, such as handwritten characters.
• Image clarity, quality and range of the pen ink plotted.
• Written text stroke, clarity, and thickness of the text.
• Pen or pencil ink injecting ratio on the paper.
• Local variations, rounded corners, and improper extrusions
• Unreflective and relative size of the character.
• Some characters represents different shapes
• In the translation point of view, it can be entirely or partly and it represents relative shift
of the character.
• Individual and irrelative line pixels, segments and curves.
4. GENERAL TELUGU CHARACTERS, NUMBERS AND SYMBOLS
Figure 1. Telugu characters and numbers
5. Computer Science & Information Technology (CS & IT) 79
Figure 2. Combinational vowels with consonants
Figure 3. Conjunct consonants
5. PAPER OBJECTIVES
The main objectives of this paper is To determine suitable features for decision making state and
identification of Telugu Written and Printed Consonants and Conjunct Consonants based on 3
layer approach with their orientation, alignment method. And also to develop the identification,
classification and deference prototyping for written and printed mixed and conjunct Consonants
characters with their orientation, alignment method by using fuzzy logic system.
6. WORKING METHODOLOGY
The scanned image page contains the different stroke levels of consonants, normal and mixed
conjunct consonants. In this research paper we are concentrated on different stroke levels of
6. 80 Computer Science & Information Technology (CS & IT)
consonants and mixed conjunct consonants. The scanned image has been processed under the
following processes.
• Hand written Text in image identification and detection.
• Hand written Text layout or orientation identification.
• Text classification of the text based on the orientation of the text
• Segmentation.
• Character processing applying the Gaussian Fuzzy process
• Post Processing Analysis.
In this implementation Fuzzy logic based neural network having the four methods (input,
fuzzification, inference and defuzzification) have been used. Fuzzification is the scaling of input
data to the universe of discourse (the range of possible values assigned to fuzzy sets). Rule
application is the evaluation of fuzzified input data against the fuzzy rules written specifically for
the system. Defuzzification is the generation of a specific output value based on the rule strengths
that emerge from the rule application. The Fuzzy based neural network rules can be applied for
the paper front and back side layered written and printed characters.
This paper implements the aadvanced ffuzzy llogic system controller collected the text in the
form of written or printed, collected the text images from the scanned file, digital camera,
Processing the Image with examine the high intensity of images based on the quality ration,
extract the image characters depends on the quality then check the character orientation and
alignment then to check the character thickness, base and print ration. The input image characters
can classify into the two ways, first as normal consonants and second as conjunct consonants,
first way represents the normal consonants and the second way represents conjunct consonants.
In this research, we classify input image written and printed as normal consonants and second as
conjunct consonants based on the 3 layer approach. The middle layer represents normal
consonants and the top and bottom layer represents conjunct consonants. Here recognition
process starts from middle layer, and then it will check the top and bottom layers, the recognition
process treat as conjunct consonants when it can detect any symbolic characters in top and bottom
layers of present base character otherwise treats as normal consonants as shown in the fig.5.
Capture the entire consonant or conjunct consonant characters from 3 layers Middle, Top,
Bottom(MTB) into single character or symbol. After conversion it into single symbolic character,
then the concern algorithmic methodology can be applied to identify the realistic name of the
character. In this methodology to classify the consonants and conjunct consonants proposed
concern algorithmic methodology can be applied in second level.
Figure 4. Examples and Tested Samples of handwritten Telugu characters with different modes.
7. Computer Science & Information Technology (CS & IT) 81
Figure 5. Tested Samples of handwritten Telugu characters with layers to help the sensor to detect variation
of the written character type.
We are applied the post processing technique to all 3 layer characters. Then after in Post
processing of the image, we are concentrated on the Image text readability and compatibility. If
the readability is not process then repeat the process again as shown data flow structure fig.6. In
this recognition process includes slant correction, thinning, normalization, segmentation, feature
extraction and classification.
Top Layer
Middle Layer
Bottom Layer
Mixed Conjunct Consonant
8. 82 Computer Science & Information Technology (CS & IT)
Figure 6. Data Process and next level flow diagram in OCR process.
In the process of development of the algorithm the pre-processing, segmentation, character
recognition and post-processing modules were discussed. The main objectives to the development
of this paper are: To develop the classification, identification of deference prototyping for Written
and Printed Consonants, Conjunct Consonants and symbols based on 3 layer approach with
different measurable area by using fuzzy logic and to determine suitable features for handwritten
character recognition.
7. IMPLEMENTATION
The Fuzzy logic was for the most part an object of skepticism and derision, in part because the
word ‘‘fuzzy” is generally used in a pejorative sense. Fuzzy logic is not fuzzy. Basically, fuzzy
logic is a precise logic of imprecision and approximate reasoning. More specifically, fuzzy logic
may be viewed as an attempt at formalization/mechanization of two remarkable human
False
True, means quality ration is suitable to identify the page back sided text
and symbols
True
False
Extract image depends on the quality and it is ready
to read the machine.
Post processing of the image:
Machine can be read the characters
and text depends on the keystrokes
of text.
If the readability is
not process then
repeat the process
again.
Image text readability and
compatibility is sufficient to
read the text
Examine the intensity of images based
on the quality ration.
Processing the Image Stage 1
Stage 2
Take the text in the form of
written or printed.
Take the text images from the digital
camera/scanned file.
Page Front side text Quality <
Page Back side text Quality
Processing of the image - Stage 2
The character orientation, thickness, base, stroke, print
ration and alignment
Observed and verified. is checked.
Extract image depends on the quality.
9. Computer Science & Information Technology (CS & IT) 83
capabilities. First, the capability to converse, reason and make rational decisions in an
environment of imprecision, uncertainty, incompleteness of information, conflicting information,
partiality of truth and partiality of possibility – in short, in an environment of imperfect
information. And second, the capability to perform a wide variety of physical and mental tasks
without any measurements and any computations [1].
The three elements required to realize a fuzzy system are fuzzification, rule application, and
defuzzification. Fuzzification is the scaling of input data to the universe of discourse (the range of
possible values assigned to fuzzy sets). Rule application is the evaluation of fuzzified input data
against the fuzzy rules written specifically for the system. Defuzzification is the generation of a
specific output value based on the rule strengths that emerge from the rule application.
In a realized fuzzy system, a microcontroller or other engine runs a linked section of object code
that consists of two segments. One segment implements the fuzzy logic algorithm, performing
fuzzification, rule evaluation, and defuzzification, and thus can be thought of as a generic fuzzy
logic inference engine. The other segment ties the expected fuzzy logic inputs and outputs, as
well as application-specific fuzzy rules, to the fuzzy logic inference engine[1] as shown in the
Figure 7.
Figure 7. Basic block diagram of Fuzzy System Crisp inputs and Outputs.
One may ask where and how fuzzy logic is implemented. here with the layer quadrants location
and three layered quadrants differentiation method for consonants and conjunct consonants with
the set of rules are known and which are the feeds for fuzzy logic controller [8 - 9] fuzzification
rules, these cases and conditions would be implemented as the if cases and for each individual
quadrant the processing action is to be done is written as the then-corresponding action Fuzzy-
neural network having the four layers (input, fuzzification, inference and defuzzification) have
been used.
7.1 Basic Configuration of a Fuzzy System
Fuzzy controller in a closed-loop configuration (top panel) consists of dynamic filters and a static
map (middle panel). The static map is formed by the knowledge base, inference mechanism and
fuzzification and defuzzification interfaces.
10. 84 Computer Science & Information Technology (CS & IT)
Figure 8. Fuzzy controller in a closed-loop configuration (top panel) consists of dynamic filters and a static
map (middle panel). The static map is formed by the knowledge base, inference mechanism and
fuzzification and defuzzification interfaces.
7.2 Fuzzy Sets
Fuzzy sets can be effectively used to represent linguistic values, such as low, young, and
complex. A fuzzy set can be defined mathematically by assigning to each possible individual in
the universe of discourse a value representing its grade of membership in the fuzzy set to a
greater or lesser degree as indicated by a larger or smaller membership grade. The fuzzy set is
represented as where x is an element in X and µA(x) is a membership function of set A which
defines the membership of fuzzy set A in the universe of discourse, X.
7.3 Fuzzy Membership Functions
A fuzzy set is characterized by a membership function which associates with each point in the
fuzzy set a real number in the interval [0, 1], called degree or grade of membership. The
membership function may be triangular, trapezoidal, Gaussian etc. A triangular membership is
described by a triplet (a, m, b), where „m‟ is the modal value, „a‟ and „b‟ are the right and left
boundary respectively. The trapezoidal membership function (shown in Figure. 9) is defined as
follows.
11. Computer Science & Information Technology (CS & IT) 85
Figure 9. Trapezoidal Membership
Function for µ Z (xk, γ k)
Another fuzzy membership function that is often used to represent vague, linguistic terms is the
Gaussian which is called Gaussian membership function (shown in figure 10) is defined as
follows.
Figure 10. Gaussian Membership Function for µ Z(xk, γ k)
7.4 Gaussian Bell curve sets
Give richer fuzzy system with simple learning laws that tune the bell curve variance. The
Gaussian Function is represented by “(equation 1),”
12. 86 Computer Science & Information Technology (CS & IT)
Where Ci is the center of the ith
fuzzy set and is the width of the ith
fuzzy set.
Figure 11. Representation of DATA Cost driver using Gaussian Membership Function
We define a fuzzy set for each linguistic value with a Gaussian shaped membership function µ is
shown in Figure 10. We have defined the fuzzy sets corresponding to the various associated
linguistic values for each variable / parameter of interest it may be character intensity, orientation,
layout or anything.
In this research, a new fuzzy effort estimation model is proposed by using Gaussian function to
deal with linguistic data or text image with three layered quadrant position analysis, and to
generate fuzzy membership functions and rules for further processing the membership functions
Primitives have been added to find form a character, which is part of the lexicon. The word is not
said to be recognized till it is tested with lexicon containing root words with an efficient
algorithm [7]. The system working model is designed as shown in the flowchart figure 6, and the
process the recognition the consonants and conjunct consonants based the figure 8, figure 9,
figure.10 and figure.11 is repeated till the whole text is reached to get the clarity and in readable
and understandable.
8. DISCUSSION
The proposed algorithmic data flow presented to get the versatility in implementation while
constructing an OCR system; our proposal system can scan the text in different layered approach
with their different directions and orientation. There are many advantages of the proposed system.
First, when there are some feature parts which are related to knowing the decision making stages
and identification of mixed hand written letters and consonant character and the Second,
identification of low rate and low quality written mixed conjunct consonant text.
9. FUTURE SCOPE
The proposed 3 layered methodology approach planning to test and it can be implementing either
using the math tool or the LabVIEW VI GUI and MathLab. Our future work aims to improve the
classifier of the mixed and non-mixed conjunct consonants to achieve still better recognition rate
with our future proposal algorithmic methodology and also to improve the better recognition
procedure for low quality readable imaged Telugu mixed-conjunct-consonants.
13. Computer Science & Information Technology (CS & IT) 87
ACKNOWLEDGEMENTS
I Dr B.Rama would like to thank everyone.
I Mr Santosh Kumar Henge would like to thank everyone.
REFERENCES
[1] Lotfi A. Zadeh.: Is there a need for fuzzy logic? Department of EECS, University of California,
Berkeley, CA 94720- 1776, United States, 8 February 2008; 25 February 2008.
[2] H. Swethalakshmi, Anitha Jayaraman, V. Srinivasa Chakravarthy, C. ChandraSekhar, : Online
Handwritten Character Recognition of Devanagari and Telugu Characters using Support Vector
Machines, Department of Computer Science and Engineering, Department of Biotechnology, Indian
Institute of Technology Madras, Chennai - 600 036, India.
[3] RAZALI BIN ABU BAKAR,: Development of Online Unconstrained Handwritten Character
Recognition Using Fuzzy Logic, Universiti Teknologi MARA.
[4] Fuzzy Logic Toolbox User’s Guide, The MathWorks Inc., 2001.
[5] Santosh Kumar Henge, Laxmikanth Ramakrishna, Niranjan Srivastava,: Advanced Fuzzy Logic
controller based Mirror- Image-Character-Recognition OCR, The Libyan Arab International
Conference on Electrical and Electronic 3101/01/32-32 Engineering LAICEEE. 3101/01/32-32. Pg
261 -268.
[6] P.Vanaja Ranjan,: Efficient Zone Based Feature Extration Algorithm for Hand Written Numeral
Recognition of Four Popular South Indian Scripts, Journal of Theoretical and Applied Information
Technology. pg 1171-1181.
[7] RAZALI BIN ABU BAKAR,: Development of Online Unconstrained Handwritten Character
Recognition Using Fuzzy Logic, Universiti Teknologi MARA.
[8] P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, and C. Kimpan: Off-Line Hand
Written Thai Character Recognition using Ant-Miner Algorithm. World Academy of Science,
Engineering and Technology, 8, 2005, Pg 276-281.
[9] Mr.Danish Nadeem & Miss.Saleha Rizvi,: Character Recognition using Template Matching.
DEPARTMENT OF COMPUTER SCIENCE, JAMIA MILLIA ISLAMIA NEW DELHI-25.
[10] Ch. Satyananda Reddy, KVSVN Raju,: An Improved Fuzzy Approach for COCOMO‟s Effort
Estimation using Gaussian Membership Function JOURNAL OF SOFTWARE, VOL. 4, NO. 5,
JULY 2009, pp 452-459.
[11] L.A. Zadeh,: Outline of a new approach to the analysis of complex systems and decision processes,
IEEE Transaction on Systems Man and Cybernetics SMC-3 (1973) 28–44.
[12] L.A. Zadeh,: Generalized theory of uncertainty(GTU)–principal concepts and ideas, Computational
Statistics & Data Analysis 51 (2006) 15–46.
[13] L.A. Zadeh,: On the analysis of large scale systems, in: H. Gottinger (Ed.), Systems Approaches and
Environment Problems, Vandenhoeck and Ruprecht, Gottingen, 1974,pp. 23–37.
[14] L.A. Zadeh,: A fuzzy-algorithmic approach to the definition of complex or imprecise concepts,
International Journal of Man–Machine Studies 8 (1976) 249–291.
[15] L.A. Zadeh,: From imprecise to granular probabilities, Fuzzy Sets and Systems 154 (2005) 370–374.
14. 88 Computer Science & Information Technology (CS & IT)
[16] L.A. Zadeh,: Toward a perception-based theory of probabilistic reasoning with imprecise
probabilities, Journal of Statistical Planning and Inference 105 (2002) 233–264.
[17] I. Perfilieva,: Fuzzy transforms: a challenge to conventional transforms, in: P.W. Hawkes (Ed.),
Advances in Images and Electron Physics, vol.147, Elsevier Academic Press, San Diego 2007,
pp.137-196.
[18] A.P. Dempster,: Upper and lower probabilities induced by a multivalued mapping, Annals of
Mathematical Statistics 38 (1967) 325-329.
[19] G. Shafer,: A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976
[20] D. Schum,: Evidential Foundations of Probabilistic Reasoning, Wiley & Sons, 1.
[21] Purna Vithlani , Dr. C. K. Kumbharana, : A Study of Optical Character Patterns identified by the
different OCR Algorithms, International Journal of Scientific and Research Publications, Volume 5,
Issue 3, ISSN 2250-3153 March 2015 .
[22] Rohit Verma and Dr. Jahid Ali, : A-Survey of Feature Extraction and Classification Techniques in
OCR Systems, International Journal of Computer Applications & Information Technology, Vol. 1,
Issue 3, November 2012.
[23] Richa Goswami and O.P. Sharma, : A Review on Character Recognition Techniques, IJCA, Vol. 83,
No. 7, December 2013.
[24] Ms.M.Shalini, Dr.B.Indira, : Automatic Character Recognition of Indian Languages – A brief
Survey, IJISET, Vol. 1, Issue 2, April 2014.
[25] José C. Principe, Neil R. Euliano, Curt W. Lefebvre: Neural and Adaptive Systems: Fundamentals
Through Simulations”, ISBN 0-471-35167
AUTHORS
Dr B.RAMA, She received her Ph.D. Degree in Computer Science from Padmavati
Mahila Visvavidyalayam (Padmavati Women’s University), Thirupathi-India in the year
of 2009. She is working as Assistant Professor in Computer Science since six years at
Department of Computer Science, University Campus College, Kakatiya University.
She was the Chairperson, Board of Studies in Computer Science from 2013-15. She is
having total 11 years of Teaching Experience in Engineering Colleges. She is author or
co-author around 20 scientific papers mainly in IEEE international Conferences and
International Journals. Her area of interest is Artificial Intelligence and Data Mining.
SANTHOSH KUMAR HENGE, He received his M.Phil. Degree in Computer Science
from Periyar University, Salem – presently he is working as Associate Professor in
Computer Science. He is having very good International level teaching experience.
Previously, He worked in various countries Maldives, Libya, Oman and Ethiopia with
different level of positions. He was published more than 16 research papers in
International Journals and Conference Proceedings. He is doing his research in the field
of Artificial Intelligence-Neuro based Fuzzy System. His area of interest is Artificial
Intelligence and Data Mining.