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 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.
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.
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 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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
Artificial Neural Network / Hand written character RecognitionDr. Uday Saikia
1. Overview
2.Development of System
3.GCR Model
4.Proposed model
5.Back ground Information
6. Preprocessing
7.Architecture
8.ANN(Artificial Neural Network)
9.How the Human Brain Learns?
10.Synapse
11.The Neuron Model
12.A typical Feed-forward neural network model
13.The neural Network
14.Training of characters using neural networks
15.Regression of trained neural networks
16.Training state of neural networks
17.Graphical user interface….
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.
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.
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.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
OCR processing with deep learning: Apply to Vietnamese documents Viet-Trung TRAN
This document discusses using deep learning techniques like LSTM and CTC for optical character recognition (OCR), specifically for Vietnamese documents. It provides an overview of OCR, the history including Tesseract, and challenges with traditional approaches. Connectionist temporal classification (CTC) is introduced as a way to directly train RNNs on unsegmented sequence data. CTC combined with LSTM networks allows for end-to-end training of OCR without needing pre-segmented text. The document demonstrates how this approach can be applied to perform OCR on Vietnamese documents.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
HANDWRITTEN DIGIT RECOGNITION USING k-NN CLASSIFIERvineet raj
This document proposes using a k-nearest neighbor classifier to recognize handwritten digits from the MNIST database. It discusses existing methods that use star-layered histogram feature extraction and class-dependent feature selection, which achieve accuracies of around 93% and 92% respectively. However, these methods require thinning operations or have high computational costs. The document proposes using k-NN classification with pre-processing and feature extraction to achieve higher accuracy of around 96% with lower computation requirements than existing models.
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.
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.
OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HA...csandit
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.
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.
Handwritten digit recognition using image processing anita maharjan
The document presents a case study on handwritten digit recognition using image processing and neural networks. It discusses collecting handwritten digit images, preprocessing the images by cutting, resizing and extracting features, and then training a neural network using backpropagation to recognize the digits. The system aims to recognize handwritten digits for applications like signature, currency and number plate recognition. It concludes that understanding neural networks makes it easier to apply such intelligent recognition to machines.
Design and Development of a 2D-Convolution CNN model for Recognition of Handw...CSCJournals
Owing to the innumerable appearances due to different writers, their writing styles, technical environment differences and noise, the handwritten character recognition has always been one of the most challenging task in pattern recognition. The emergence of deep learning has provided a new direction to break the limits of decades old traditional methods. There exist many scripts in the world which are being used by millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. Feature based approaches derive important properties from the test patterns and employ them in a more sophisticated classification model. Feature extraction using Zernike moment and Polar harmonic transformation techniques was also performed and a moderate classification accuracy was also achieved. The problems faced while using these techniques led us to use CNN based recognition approach which is capable of learning the feature vector from the training character image samples in an unsupervised manner in the sense that no hand-crafting is employed to determine the feature vector. This paper presents a deep learning paradigm using a Convolution Neural Network (CNN) which is implemented for handwritten Gurumukhi and devanagari character recognition (HGDCR). In the present experiment, the training of a 34-layer CNN for a 35 class self-generated handwritten Gurumukhi and 60 class (50 alphabet and 10 digits) handwritten Devanagari character dataset was performed on a GPU (Graphic Processing Unit) machine. The experiment resulted with an average recognition accuracy of more than 92% in case of Handwritten Gurumukhi Character dataset and 97.25% in case of Handwritten Devanagari Character dataset. It was also concluded that the training and classification through our network design performed about 10 times faster than on a moderately fast CPU. The advantage of this framework is proved by the experimental results.
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
IRJET- Survey on Generating Suggestions for Erroneous Part in a SentenceIRJET Journal
This document discusses using deep learning approaches like long short-term memory (LSTM) neural networks to generate suggestions for erroneous parts of sentences in Indian languages. Indian languages pose unique challenges due to their morphological richness and structure differences from English. The document reviews natural language processing techniques like recurrent neural networks, convolutional neural networks, and LSTMs. It proposes using LSTMs to model sentence structure and generate possible corrections for errors in an unsupervised manner. The goal is to develop this technique for morphologically complex Indian languages like Malayalam.
There are two kinds of research according to purpose: basic research and applied research. Basic research seeks to create new knowledge without directly addressing practical problems, while applied research aims to solve practical problems through techniques like effectiveness studies. Both kinds of research contribute to new understandings or improvements, whether by exploring normal processes or determining solutions.
Artificial Neural Network / Hand written character RecognitionDr. Uday Saikia
1. Overview
2.Development of System
3.GCR Model
4.Proposed model
5.Back ground Information
6. Preprocessing
7.Architecture
8.ANN(Artificial Neural Network)
9.How the Human Brain Learns?
10.Synapse
11.The Neuron Model
12.A typical Feed-forward neural network model
13.The neural Network
14.Training of characters using neural networks
15.Regression of trained neural networks
16.Training state of neural networks
17.Graphical user interface….
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.
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.
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.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
This document presents a case study on using deformable templates for recognizing handwritten digits. Deformable templates match unknown images to known templates by deforming the contours of templates to fit the edge strengths of unknown images. The dissimilarity measure is derived from the deformation needed for the match. This technique achieved recognition rates up to 99.25% on a dataset of 2,000 handwritten digits. Statistical and structural features are extracted to represent characters for classification using deformable templates.
OCR processing with deep learning: Apply to Vietnamese documents Viet-Trung TRAN
This document discusses using deep learning techniques like LSTM and CTC for optical character recognition (OCR), specifically for Vietnamese documents. It provides an overview of OCR, the history including Tesseract, and challenges with traditional approaches. Connectionist temporal classification (CTC) is introduced as a way to directly train RNNs on unsegmented sequence data. CTC combined with LSTM networks allows for end-to-end training of OCR without needing pre-segmented text. The document demonstrates how this approach can be applied to perform OCR on Vietnamese documents.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
HANDWRITTEN DIGIT RECOGNITION USING k-NN CLASSIFIERvineet raj
This document proposes using a k-nearest neighbor classifier to recognize handwritten digits from the MNIST database. It discusses existing methods that use star-layered histogram feature extraction and class-dependent feature selection, which achieve accuracies of around 93% and 92% respectively. However, these methods require thinning operations or have high computational costs. The document proposes using k-NN classification with pre-processing and feature extraction to achieve higher accuracy of around 96% with lower computation requirements than existing models.
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.
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.
OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HA...csandit
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.
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.
Handwritten digit recognition using image processing anita maharjan
The document presents a case study on handwritten digit recognition using image processing and neural networks. It discusses collecting handwritten digit images, preprocessing the images by cutting, resizing and extracting features, and then training a neural network using backpropagation to recognize the digits. The system aims to recognize handwritten digits for applications like signature, currency and number plate recognition. It concludes that understanding neural networks makes it easier to apply such intelligent recognition to machines.
Design and Development of a 2D-Convolution CNN model for Recognition of Handw...CSCJournals
Owing to the innumerable appearances due to different writers, their writing styles, technical environment differences and noise, the handwritten character recognition has always been one of the most challenging task in pattern recognition. The emergence of deep learning has provided a new direction to break the limits of decades old traditional methods. There exist many scripts in the world which are being used by millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. Feature based approaches derive important properties from the test patterns and employ them in a more sophisticated classification model. Feature extraction using Zernike moment and Polar harmonic transformation techniques was also performed and a moderate classification accuracy was also achieved. The problems faced while using these techniques led us to use CNN based recognition approach which is capable of learning the feature vector from the training character image samples in an unsupervised manner in the sense that no hand-crafting is employed to determine the feature vector. This paper presents a deep learning paradigm using a Convolution Neural Network (CNN) which is implemented for handwritten Gurumukhi and devanagari character recognition (HGDCR). In the present experiment, the training of a 34-layer CNN for a 35 class self-generated handwritten Gurumukhi and 60 class (50 alphabet and 10 digits) handwritten Devanagari character dataset was performed on a GPU (Graphic Processing Unit) machine. The experiment resulted with an average recognition accuracy of more than 92% in case of Handwritten Gurumukhi Character dataset and 97.25% in case of Handwritten Devanagari Character dataset. It was also concluded that the training and classification through our network design performed about 10 times faster than on a moderately fast CPU. The advantage of this framework is proved by the experimental results.
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03.mastering arabic script a guide to handwritingMohammad Ali
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A Review on Geometrical Analysis in Character Recognitioniosrjce
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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
This document provides a comprehensive review of offline handwritten character recognition. It discusses the various phases of a character recognition system, including image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing techniques like noise removal, binarization, and size normalization are described. Common feature extraction methods like statistical, transform, and structural features are also summarized. Finally, the document analyzes different classification approaches used in character recognition, such as neural networks, support vector machines, and multiple classifier methods.
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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.
This document summarizes research on recognizing online handwritten Sanskrit characters using support vector classification. It discusses using Freeman chain code to extract features from character images and represent boundary pixels. A randomized algorithm generates the chain codes. Features vectors are then built and used to train a support vector machine classifier. Segmentation is also used to evaluate possible segmentation zones. The goal is to develop an accurate system for recognizing Sanskrit characters, which is challenging due to complex character shapes and styles. Previous work on character recognition is discussed, focusing on Indian scripts like Devanagari and techniques like feature extraction and classification.
A Survey on Tamil Handwritten Character Recognition using OCR Techniquescscpconf
This document summarizes research on recognizing Tamil handwritten characters using optical character recognition (OCR) techniques. It discusses various approaches that have been used for pre-processing images, segmenting characters, extracting features, and classifying characters. For pre-processing, techniques like binarization, noise removal, normalization, and skew correction are discussed. For segmentation, methods like projection-based, smearing, and graph-based techniques are mentioned. Feature extraction approaches include statistical, structural, and hybrid methods. Classification is done using techniques such as k-nearest neighbors, neural networks, and support vector machines. The document also provides two tables summarizing accuracy levels achieved by different studies on Tamil character recognition and the limitations of those studies.
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.
DEVNAGARI NUMERALS CLASSIFICATION AND RECOGNITION USING AN INTEGRATED APPROACHijfcstjournal
Character recognition has always been a challenging field for the researchers. There has been an astounding progress in the development of the systems for character recognition. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like preprocessing, segmentation, recognition and post processing. The recognition generally, consists of feature extraction and classification. The choice of features and classification scheme affects the performance of OCR largely. In this paper, a classification scheme is proposed for the Devnagari numerals, which forms the basis for recognition. This approach integrates the structural features and water reservoir analogy based feature to classify the Devnagari numeral. In order to classify a single numeral, at most four checks are required. This increases the efficiency of the proposed scheme.
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.
Optical Character Recognition from Text ImageEditor IJCATR
Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten
characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into
corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with
spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of
character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase
letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact
character using feature matching between the extracted character and the template of all characters as a measure of similarity.
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.
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.
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.
The Heuristic Extraction Algorithms for Freeman Chain Code of Handwritten Cha...Waqas Tariq
Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. In HCR, there are many representation schemes and one of them is Freeman chain code (FCC). Chain code is a sequence of code direction of a characters and connection to a starting point which is often used in image processing. The main problem in representing character using FCC that it is depends on the starting points. Unfortunately, the study about FCC extraction using one continuous route and to minimizing the length of chain code to FCC from a thinned binary image (TBI) have not been widely explored. To solve this problem, heuristic algorithms are proposed to extract the FCC that is correctly representing the characters. This paper proposes two heuristics algorithm that are based on randomized and enumeration-based algorithms to solve the problems. As problem solving techniques, the randomized algorithm makes the random choices while enumeration-based algorithm enumerates all possible candidates for solution. The performance measures of the algorithms are the route length and computation time. The experiment on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. The experimental result shows that route length of both algorithms are similar but the computation time of enumeration-based algorithm is higher than randomized algorithm. This is because enumeration-based algorithm considers all branches in route walk.
Design and Description of Feature Extraction Algorithm for Old English FontIRJET Journal
This document describes a proposed feature extraction algorithm for recognizing Old English font characters. It consists of four main stages: data collection, preprocessing, feature extraction, and recognition using a minimum distance classifier. For preprocessing, binarization and noise removal are performed. The feature extraction algorithm extracts 20 features from each character image by dividing it into 16x16 zones and identifying black pixels in each zone. Classification is done using a minimum distance classifier to match features to class means. The method achieved a 79% recognition rate on the test data.
IRJET - A Survey on Recognition of Strike-Out Texts in Handwritten DocumentsIRJET Journal
This document summarizes research on recognizing strike-out or crossed-out text in handwritten documents. It discusses 4 papers that studied this problem in different languages like English, Bengali, and Devanagari. The first paper used an LSTM model to recognize struck-out English text with 11% character error rate. The second used HMMs to recognize French text crossed out with lines or waves, achieving 46-92% accuracy. The third used graph algorithms and SVM to detect and remove Bengali strike-outs, obtaining 95% accuracy. The fourth used decision trees to classify English forensic documents, identifying 48% of crossed texts correctly. Overall the document reviews approaches to strike-out text recognition and removal.
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.
A Novel Approach to Recognize Handwritten Gujarati Digits.pdfSamantha Martinez
This document proposes a novel approach for recognizing handwritten Gujarati digits using structural features. It discusses preprocessing steps like noise removal, binarization, segmentation and thinning. For feature extraction, it uses structural features like bounded regions, number of endpoints, and endpoint positions. A decision tree is used for classification, with the structural features forming the branching conditions. Challenges include variability in writing styles and possibility of overlap or overwritten digits. The proposed approach aims to provide a simple classification method using key structural properties of each digit.
Comparative study of two methods for Handwritten Devanagari Numeral RecognitionIOSR Journals
Abstract : In this paper two different methods for Numeral Recognition are proposed and their results are
compared. The objective of this paper is to provide an efficient and reliable method for recognition of
handwritten numerals. First method employs Grid based feature extraction and recognition algorithm. In this
method the features of the image are extracted by using grid technique and this feature set is then compared
with the feature set of database image for classification. While second method contains Image Centroid Zone
and Zone Centroid Zone algorithms for feature extraction and the features are applied to Artificial Neural
Network for recognition of input image. Machine text recognition is important research area because of its
applications in many areas like Bank, Post office, Hospitals etc.
Keywords: Handwritten Numeral Recognition, Grid Technique, ANN, Feature Extraction, Classification.
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1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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A Comprehensive Study On Handwritten Character Recognition System
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. IV (Mar – Apr. 2015), PP 01-07
www.iosrjournals.org
DOI: 10.9790/0661-17240107 www.iosrjournals.org 1 | Page
A Comprehensive Study On Handwritten Character Recognition
System
Renjini L 1
, Rubeena B2
1.2
(Computer science & Engineering, College of Engineering Karunagappally/ Cusat, India)
Abstract: Nowadays handwritten character recognition is still remain an open problem because of the
variability in writing style. Conversion of handwritten characters is important for making manuscripts into
machine recognizable form so that it can be easily accessed and preserved. Many researchers have worked in
the area of handwriting recognition and numerous techniques and models have been developed to recognize
handwritten text. The study investigates that in any character recognition system there exist three major stages
such as Preprocessing, Feature Extraction and Classification. This paper provides a comprehensive review of
existing works in offline handwritten character recognition.
Keywords: Back Propagation, Chain Code, Moment invariants, Probabilistic Neural Network, SIFT, Wavelet,
Zoning,
I. Introduction
Character Recognition (CR) is one of the most successful applications in the areas of pattern
recognition and artificial intelligence. It is the process of detecting and recognizing characters from input image
and converts it into machine recognizable form. Handwritten Character Recognition (HCR) is useful in cheque
processing in banks, almost all kind of form processing systems, handwritten postal address resolution and
many more. One of the main advantages of the CR process is that it can save both time and effort when
developing a digital replica of the document. CR process can be classified in to two categories namely Off-line
CR and On-line CR as shown in Fig1.
Fig 1.Character Recognition
The Off-line Handwritten Character Recognition focuses on recognizing character or words that had
been recorded earlier in the form of scanned image of document. In case of On-line Handwritten Character
Recognition, the process of recognition is performed at the time of writing itself through the successive points
of strokes by the writers in a fraction of time. Although Off-line and on-line CR techniques have different
approaches, they share a lot of common problems and solutions. Off-line CR is relatively more complex and
requires more research compared to on- line CR because of variability on size and writing style of handwritten
characters by different individual at different times. The character recognition system consists of three major
stages as shown in Fig2:
2. A Comprehensive Study On Handwritten Character Recognition System
DOI: 10.9790/0661-17240107 www.iosrjournals.org 2 | Page
1) Preprocessing: It improves the image data by removing unwanted noise or enhances some image features
important for further processing.
2) Feature extraction: Features define the behavior of region of interest in an image. In this stage various
techniques are applied to get features that will be useful in classification and recognition of character
images.
3) Classification: The features extracted from the above phase are given as input to the trained classifier like
Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM).
Classifier compares input features with stored pattern and find out best matching class of input.
Fig 2. Block Diagram of Character Recognition
This paper is arranged to focus on Off-line CR methodologies with respect to these three stages of CR systems.
II. Literature Review
A number of researches have been proposed over the years for character recognition. This section
highlights the methods under each of the three phases of character recognition and thus provides an overview of
various literatures based on character recognition. Before moving on to the first stage of character recognition
system, character image can be acquired by online or offline mode. In offline mode handwritten character are
acquired by scanning the documents or capturing photographs of documents.
A. Preprocessing
The process of extracting text from the document is called preprocessing. The accuracy of text
recognized by means of OCR depends on the factors such as scanner quality, scan resolution, paper quality,
fonts used, linguistic complexities and so on. The problems that may occur due to these factors can be solved by
using preprocessing techniques. It enhances character image preparing it for later stages and its objectives are
noise reduction, data normalization and compression of information to be retained. An OCR system can be
made more robust by applying some of the effective preprocessing techniques such as image enhancement
techniques, binarization, noise reduction techniques, skew detection and correction, character segmentation,
image normalization and morphological processing.
B.V. Dhandra et al. [1] have performed series of operations like binarization, filtering and
morphological operations as part of preprocessing for recognizing Kannada handwritten vowels. Here gray
scale image is binarised using Otsu’s global thresholding and median filter is applied for removing noise due to
erratic hand movements and digitization inaccuracies. After this, morphological operations are performed for
removing isolated locations and spikes around the end of the vowels. Even if median filter overcomes the
limitations of the linear filters it may cause removal of corners and threads, blurring of texts in the documents.
In order to overcome these limitations Kanika Bansal et al. [2] proposed an algorithm named K-algorithm,
which is the combination of filtering and binarization. For reducing within- class variation of shape of
unconstrained handwritten numerals, Suzete E. N. Correia et al.[3] have used slant and size normalization as
preprocessing methods. Ntogas Nikolaos et al.[4] proposed four Binarization methods such as Otsu’s, Niblack,
Sauvola’s and Bernsen’s for discriminating degraded and very poor quality gray scale Byzantine manuscript
from the background based on pure thresholding and filtering. As per his work Otsu’s provide better result as
3. A Comprehensive Study On Handwritten Character Recognition System
DOI: 10.9790/0661-17240107 www.iosrjournals.org 3 | Page
compared to other three methods. In [5] Xiang Zhao et.al used morphological operations (thinning,
skeletonization etc.) for recognizing characters from map.
B. Feature Extraction
In this phase, features of individual character are extracted. The performance of any character
recognition system depends on the features that are extracted. The extracted features of input character should
allow its classification in a unique way. Feature Extraction serves two purposes, one is to extract properties that
can identify a character uniquely. Second is to extract properties that can differentiate between similar
characters. This phase of character recognition system is very problem dependent. Good features are those
whose values are similar for objects belonging to the same category and distinct for objects in different
categories. Selection of an appropriate feature extraction method is the most important factor in achieving high
recognition performance. In feature extraction stage each character is represented as a feature vector, which
becomes its identity. The major goal of feature extraction is to extract a set of features which maximizes the
recognition rate with the least amount of elements. Therefore, better features that are able to recognize
characters distinctively must be extracted. Different feature extraction methods are Global Transformation and
Series Expansion, Statistical Representation, Geometrical and Topological Representation.
i) Global Transformation and Series Expansion
The linear combination of a sequence of simpler functions provides a compact representation of
continuous signal known as transformation and series expansion. Global transformation features are calculated
by converting image in frequency domain like Discrete Fourier Transformation (DFT), Discrete Cosine
Transformation (DCT), Discrete Wavelet Transformation (DWT), Walsh- Hadamard Transformation etc. Some
related methods used in CR field are Fourier transform, Wavelets, Moments and Gabor transform.
Wavelet transformation is a mathematical technique that decomposes the signal into series of small
basis function called wavelets. It allows the multiresolution analysis of image and is well localized in both time
and frequency domain. As a result of wavelet transformation the image is decomposed into low frequency and
high frequency components. The information content of these sub images that corresponds to Horizontal,
Vertical and Diagonal directions implies unique feature of an image. Moreover it is efficient than Fourier
transform which faces the resolution problem and localized in frequency domain only.
B. V. Dhandra et al [1] developed an algorithm for extracting features from Kannada characters. In
this algorithm, two level forward wavelet packet transform is applied to the character image using db4 filter.
Then count the number of zero crossings, the position of sharp variation points in an image, out of the resulting
sub bands and this number is taken as a feature vector. By using this concept of zero crossings of discrete
dyadic wavelet transform, Xian Zhao et al [5] have recognized character in scanning map and it is considered as
the primary investigation in this field. Diego Romero et al [6] applied different continuous wavelet transforms
to handwritten numerals to extract multiscale features such as orientation, gradient and curvature. George S
Kapogiannopoulous et al [7] applied biorthogonal discrete wavelet transform to decompose the curvature
function which characterizes the contour of the handwritten character image. Wavelet representation of
curvature function has the advantages that variations in the shape of the curve will cause only minor changes in
the wavelet representation.
In [8] Joohun Lim et al. have presented a comparative analysis of scale invariant feature extraction
using different wavelet bases. This paper shows that Gabor wavelet basis function extracts image features more
efficiently than Haar, Daubechies basis function in wavelet bases. Gabor wavelet means Gaussian enveloped
orthogonal basis function and it uses five different scales and eight orientations. In [9] Lee et al. have extracted
features using Haar orthogonal wavelet at one resolution level. When Suzete E. N. Correia et al [3] used Cohen-
Daubechies- Feauveau (CDF) family of bi- orthogonal spline wavelets as feature extractor he finds that the
recognition rate obtained with CDF 3/7 is superior to that of Haar wavelet. The Cohen- Daubechies- Feauveau
(CDF) family of bi- orthogonal spline wavelets has special properties such as short support and regularity which
is useful for off-line recognition of unconstrained handwritten numerals.
Wavelet based approaches are becoming increasingly popular in pattern recognition and have recently
been applied to character recognition. In wavelet theory, there is a large variety of wavelet bases to choose
from. Obviously, the choice of the best wavelet is dependent on the application. Recently, some papers have
been published using wavelet transform as a feature extractor for handwritten character recognition because of
its multiresolutional analysis property.
I K Pathan et al. have proposed an off-line approach for handwritten isolated Urdu characters in their
work mentioned in [10]. Authors have used moment invariants (MI) feature to recognize the characters. MI
features are well known to be invariant under rotation, translation, scaling and reflection. These features are
measure of the pixel distribution around the center of gravity of character and it captures the global character
shape information. V Karthikeyan [11] proposed a system for recognizing Tamil characters. In his paper, the
4. A Comprehensive Study On Handwritten Character Recognition System
DOI: 10.9790/0661-17240107 www.iosrjournals.org 4 | Page
character image skeletonised using Hilditch’s algorithm and features are extracted based on the concept of
image moment which is the weighted average of entire pixel intensities. Here four features are extracted from
each of the character, the equation of which is derived from Hu’s moment invariants [12].
ii) Statistical Representation
Statistical methods are based on the probability theory and hypothesis. Statistical distribution of pixels
of an image takes care of variations in writing styles. In this approach, a character image is represented using a
set of n features which can be considered as a point in n-dimensional feature space. The main goal of feature
selection is to construct linear or non-linear decision boundaries in feature space that correctly separate the
character images of different classes. The major statistical features used for character representation are zoning,
projection profiles and Crossings and distances.
In zoning, the character is divided into several overlapping or non overlapping zones of predefined
sizes. Then features such as average pixel density, histogram and sum squared distance are extracted from each
of the zones based on the percentage of black pixels present. Gradient features based method is discussed in
[13] by Ashutosh et al. where gradient vector is calculated at each pixel by means of sobel operator and then
image is divided into different zones. Then strength of gradient is accumulated in eight standard directions in
each zone.
iii) Geometrical and Topological Representation
Characters can be represented by structural features with high tolerance to distortions and style
variations. Structural features are based on topological and geometrical properties of the character such as chain
code, aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points,
horizontal curves at top or bottom, etc.
Scale Invariant Feature Transform (SIFT) is a structural descriptor which considers the local features
of an image. The speciality of the features derived as a result of implementing this algorithm is that they are
invariant to image translation, scaling and rotation. SIFT consists of four steps namely Scale space extrema
detection, Key point localization, Orientation assignment and Key point descriptor. The output of the fourth step
will be highly distinctive that are suitable for the recognition purpose.
Zahedia et al used SIFT algorithm in his paper [14] for recognizing the Arabic characters. Here the
preprocessed image is passed through SIFT algorithm in order to extract the features. As the first step of this
algorithm, the candidate key points are find out. In the next step, the key points that have low contrast and
having poor edge localization are eliminated. Then orientation is assigned to each of the localized key points for
achieving rotation invariance. In the final step, key point descriptor is created using a set of 16 histograms,
aligned in 4x4 grids, each with 8 orientation bins. As a result 128 element feature vector is obtained.
Sreeraj M et al [15] presented an approach for on-line grantha character recognition. Here features
such as time domain, writing direction and curvature are extracted. In [1] B V Dhandra used the concept of
chain code for handwritten Kannada vowel recognition. Chain codes are used to represent the boundary based
on 4-connectivity or 8-connectivity of its segments. Then the direction of each segment is coded using a
numbering scheme.
C. Classification
Classification is the process of assigning the data to their corresponding class with respect to similar
groups with the aim of discriminating multiple objects from each other within the image. Its goal is to predict
the categories of input image using its features. It is carried out on the basis of stored features in the feature
space such as structural features, global features etc. Some classification techniques used in character
recognition systems are Template Matching, Statistical Techniques, Structural Techniques, Neural Network,
and Support Vector Machine.
i) Template Matching
It is a method for finding areas of an image that match to a template image, the image patch to be
compared with the input image. According to Oivind Due Trier et al [16], this method is not well suited for
character skeleton because of the lesser chances of input image pixels to coincide with pixels of template
skeleton.
ii) Neural Network (NN)
Neural network develop its information categorization capabilities through learning process from
examples known as training samples. After getting feature space from the binary character image, an efficient
classifier is used to classify the class of a character. It is one of the commonly used classifier in handwritten
character recognition system because of their humanoid qualities such as adapting the changes and learning
5. A Comprehensive Study On Handwritten Character Recognition System
DOI: 10.9790/0661-17240107 www.iosrjournals.org 5 | Page
from prior experience. Handwritten character recognition can be implemented by using a back propagation
neural network that has been trained according to train dataset. That is, neural network recognizers learn from
an initial image training set. The trained network then makes the character identifications. One of the most
common learning methods used in this training process is called back-propagation (BP). When network is
presented with a set of training data the BP algorithm compute the difference between the actual output and
desired output and feeding back the error exist in the output and correct the weights and biases that are
responsible for the error.
Jasbir Singh et al. [17] have used Artificial Neural Network as classifier in his work for Devanagari
character recognition. ANN consist of number of processing units called neurons distributed in three layers
namely input, hidden and output that communicate with one another over a large number of weighted
connections. Such a network can be trained using sample training data and then the trained network is used to
predict the class of unknown test sample. Each output layer neurons corresponds to each class.
Seong- Whan Lee et al [9] have used Multilayer cluster neural network (MCNN) as classifier for
recognizing handwritten numerals. In MCNN the units in each layer are clustered and each cluster is fully
connected to a corresponding cluster in following layer independently. The advantage of MCNN is that it
converges in fewer iteration as compared to fully connected multilayer neural network because each sub
network of MCNN start from different initial state and learn with different multiresolution feature. Based on the
concept of MCNN, Suzete E. N. Correia et al. [3] used three layer cluster neural network for training and
classification.
In [18] D K Patel used Euclidean Distance Metric in combination with artificial neural network for
classification. For each unknown input pattern vector, distances to the mean vectors which characterize each
pattern class are computed by EDM. Minimum distance determines the class membership of input pattern
vector. In case of misclassification, the learning rule through ANN improves the recognition accuracy. G Raju
et al, used feed forward neural network for classification in his work mentioned in [19]. Based on the survey
conducted by Oivind Due Trier et al [16] Multilayer feed forward neural network have been used extensively in
optical character recognition.
Probabilistic Neural Network (PNN) is a form of radial basis function network that can be used as a
solution of pattern classification problem. To prepare a PNN classifier for pattern classification, some training is
required for the estimation of probability density function associated with classes. For PNN, training process is
faster than other neural network model such as back propagation and it is also guaranteed to converge to an
optimal direction as the size of the representative training set increases. In [1] B V Dhandra et al. have adopted
PNN as a classifier in his work for recognizing Kannada, Telugu, and Devanagari numerals.
ii) K- Nearest Neighbor Classifier (KNN)
KNN is an instance based classification algorithm where the objects are classified on the basis of
closest training examples in the feature space. Here, a test sample is assigned a same class label as that of the
majority of its K- nearest neighbors. The performance of KNN classifier depends on the proper choice of K and
the distance metric used to measure the neighbors distances.
For Kannada character recognition B V Dhandra et al[1] have used KNN as classifier. In [15] Sreeraj
M et al. have used KNN classifier where DTW (Dynamic Time Warping) is used as a distance metric in order
to enhance recognition rate. As per Jasbir Singh et al [17] KNN is simplest of all classifier for predicting the
class of the test sample.
iii) Support Vector Machine (SVM)
SVMs are a group of supervised learning methods, the goal of which is to produce a model that
predicts the target value of the test data given only the test data attributes. The standard SVM classifier takes
the set of input data and predicts to classify them in one of the only two distinct classes. SVM classifier is
trained by a given set of training data and a model is prepared to classify test data based on the trained data. In
SVM, training and classification are performed using kernel function. An SVM is a binary classifier with
discriminant function being the weighted combination of kernel functions over all training samples. The
samples of non-zero weights after learning are called support vectors which are stored and used in classification.
For recognizing Devanagari characters Rajneesh Rani et al [13] have used SVM classifier by taking
gradient based feature as its input. In [10] I K Pathan have also used SVM for training purpose based on
moment invariant features of Urdu characters. As per the comparative analysis by Jasbir Singh et al [17] on
Devanagari characters, SVM is a very useful technique for data classification as compared to ANN and KNN.
Comparison between the various literatures that is mentioned in this section is summarized in the
following table1.
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Table 1: Comparison of Various Techniques in HCR
III. Conclusion
In this paper comparative study of various phases in character recognition has been carried out. From
this literature review, Wavelet transform based features provides maximum classification accuracy compared to
other methods. It can be concluded that selection of relevant feature extraction and classification techniques
plays an important role in performance of CR system. The accurate recognition directly depends on the nature
of the material to be read and its quality. A lot can be improved in each of the phases of character recognition
system because each step contributes directly to accuracy of system.
References
[1]. B.V. Dhandra, Shashikala Parameshwarapa and Gururaj Mukarambi, Kannada Handwritten Vowels Recognition based on
Normalized Chain Code and Wavelet Filters, International Journal of Computer Applications Recent Advances in Information
Technology, NCRAIT - November 4, 2014, 21-24.
[2]. Kanika Bansal and Rajiv Kumar, K Algorithm:-A Modified Technique For Noise Removal In Handwritten Documents,
International Journal of Information Sciences & Techniques, Vol. 3 Issue 3, May 2013.
[3]. Suzete E. N. Correia and Joao M de Carvalho, Optimizing the Recognition Rates of Unconstrained Handwritten Numerals Using
Biorthogonal Spline Wavelets, Pattern Recognition, 15th International Conference on Barcelona, Published by IEEE, vol.2, 2000,
251-254.
[4]. Ntogas Nikolaos and Ventzas Dimitrios, A Binarization Algorithm For Historical Manuscripts, 12th WSEAS International
Conference on Communications, Heraklion, Greece, July 23-25, 2008, 41-51.
7. A Comprehensive Study On Handwritten Character Recognition System
DOI: 10.9790/0661-17240107 www.iosrjournals.org 7 | Page
[5]. Xian Zhao, Ping Xiao, Wavelet-Based The Character Recognition In MAP, International Conference on Integrated System for
Spatial Data Production Commission II, Volume XXXIV, PART 2, Aug.20-23, 2002, 605-608.
[6]. Diego Romero, Ana Ruedin and Leticia Seijas, Wavelet-based Feature Extraction for Handwritten Numerals, Image Analysis and
Processing ICIAP, Volume 5716, 2009, 374-383.
[7]. George S Kapogiannopoulos and Manos Papadakis, Character recognition using a biorthogonal discrete wavelet transform, Wavelet
Applications in Signal and Image Processing IV, Department of Informatics University of Athens, Hellas (Greece), Conference
Volume 2825, October 23, 1996, 384-394.
[8]. Joohyun Lim, Youngouk Kim and Joonki Paik, Comparative Analysis of Wavelet-Based Scale-Invariant Feature Extraction Using
Different Wavelet Bases, International Journal of Signal Processing, Image Processing and Pattern Recognition Communications in
Computer and Information Science Volume 61, 2009, 297-303.
[9]. Seong-Whan Lee and Young- Joon Kim, Multiresolution Recognition of Handwritten Numerals with Wavelet Transform and
Multilayer Cluster Neural Network, Document Analysis and Recognition, Proceedings of the Third International Conference on
Montreal, Published by IEEE. Vol.2, Aug 1995, 1010-1013.
[10]. Imaran Khan Pathan, Abdulbari Ahmed Bari Ahmed Ali, Ramteke R.J., Recognition of offline handwritten isolated Urdu character
International Journal on Advances in Computational Research, Vol. 4, Issue 1, 2012, pp. 117-121.
[11]. V.Karthikeyan, Hilditch’s Algorithm Based Tamil Character Recognition, International Journal of Computer Science and
Engineering Technology (IJCSET) Vol. 4, No. 03, Mar 2013, 268-273.
[12]. Ming-Kuei Hu, Visual Pattern Recognition by Moment Invariants, IRE Transactions On Information Theory, Volume:8 , issue 2
Feb 1962. 179-187.
[13]. Ashutosh Aggarwal, Rajneesh Rani, RenuDhir, Handwritten Devanagari Character Recognition Using Gradient Features,
International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5, May 2012, 85-90.
[14]. Morteza Zahedia, Saeideh Eslamia, Farsi/Arabic Optical Font Recognition Using SIFT Features, World Conference on Information
Technology Procedia Computer Science, Vol. 3, 2011, 1055-1059.
[15]. Sreeraj.M and Sumam Mary Idicula, An Online Character Recognition System to Convert Grantha Script to Malayalam, Article
Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3, Issue 7, 2012, 67-72.
[16]. Oivind Due Trier, Anil K Jain and Torfinn Taxt, “Feature Extraction Methods For Character Recognition-A Survey”, Pattern
Recognition, Volume 29, April 1996, Pages 641-662.
[17]. Jasbir Singh and Gurpreet Singh Lehal, Comparative Performance Analysis of Feature(S)- Classifier Combination for Devanagari
Optical Character Recognition System, International Journal of Advanced Computer Science and Applications (ijacsa), Volume 5,
Issue 6, 2014, 37-42.
[18]. D K Patel, T Som and M K Singh, Improving the Recognition of Handwritten Characters using Neural Network through
Multiresolution Technique and Euclidean Distance Metric, International Journal of Computer Application vol.45, no.6, May 2012,
38-50.
[19]. G. Raju and K. Revathy, Wavepackets in the Recognition of Isolated Handwritten Characters, World Congress on Engineering,
Volume 2, July 2-4, 2007.
[20]. Mansi Shah and Gordhan B Jethava, A Literature Review On Hand Written Character Recognition, Indian Streams Research
Journal, Vol -3, March 2013, 1-19.
[21]. Nafiz Arica and Fatos T Yarman-Vural, An Overview of Character Recognition Focused on Off-Line Handwriting, Applications
and IEEE Transactions on Systems, Man, and Cybernetics, Part C, Volume: 31, Issue: 2, May 2001, 216-233.
[22]. Rohit Verma and Jahid Ali, A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques,
International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013,
617-622.
[23]. Aini Najwa Azmi,Dewi Nasien and Siti Mariyam Shamsuddin, A review on handwritten character and numeral recognition for
Roman, Arabic, Chinese and Indian scripts, 20th International Conference on Pattern Recognition, ICPR , 2010.
[24]. R C Gonzalez and R E Wood, Digital Image Processing, Addison-Wesley, New York, 1992.