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.
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.
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.
Off-line English Character Recognition: A Comparative Survey
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)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
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cities. Social network users are acting as social sensors. These datasets so large and complex
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comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
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challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
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trained and validated on both training and testing data. It is observed that 96.13% words are
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data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
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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.
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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.