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Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

Semi-supervised Online Learning of Handwritten Characters Us...

引用

Asian Conference on pattern recognition (ACPR)

作者： Rituraj Kunwar Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.

关键词： Accuracy Training Handwriting recognition Character recognition Semisupervised learning Hidden Markov models

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Performance evaluation of DTW and its variants for word spotting in degraded documents

Performance evaluation of DTW and its variants for word spot...

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International Conference on Document Analysis and recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratory of Computer Science (LI) Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.

关键词： Biomedical imaging Biomedical measurement

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Semi-supervised Online Bayesian Network Learner for Handwritten Characters recognition

Semi-supervised Online Bayesian Network Learner for Handwrit...

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International Conference on pattern recognition

ISBN: (纸本)9781479952106

This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset. The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.

关键词： Accuracy Bayes methods Training Equations Niobium Learning systems Estimation

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Handwritten Bangla Compound Character recognition Using Gradient Feature

Handwritten Bangla Compound Character Recognition Using Grad...

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International Conference on Information Technology (ICIT)

作者： U. Pal T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Mie Japan

recognition of handwritten characters of Indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten compound characters using modified quadratic discriminant function (MQDF). The features used for recognition purpose are mainly based on directional information obtained from the arc tangent of the gradient. To get the feature, at first, a 2 times 2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the image. A Roberts filter is then applied on the normalized image to obtain gradient image. Next, the arc tangent of the gradient (direction of gradient) is initially quantized into 32 directions and the strength of the gradient is accumulated with each of the quantized direction. Finally, the frequencies of these directions are down sampled using Gaussian filter to get 392 dimensional feature vectors. Using 5-fold cross validation technique we obtained 85.90% accuracy from a dataset of Bangla compound characters containing 20,543 samples.

关键词： Character recognition Handwriting recognition Filters pattern recognition Filtering Automation Information technology computer vision Writing Frequency

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Scene text detection using sparse stroke information and MLP

Scene text detection using sparse stroke information and MLP

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International Conference on pattern recognition

作者： Aruni Roy Chowdhury Ujjwal Bhattacharya Swapan K. Parui Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling of the distance transform values is a novel approach. Another feature is the distance between the foreground and background colors computed in a perceptually uniform and illumination-invariant color space. Remaining features include two ratios of anti-parallel edge gradient orientations, a regularity measure between the skeletal representation and Canny edgemap of the object, average edge gradient magnitude, variation in the foreground gray levels and five others. Here, we present the results of the proposed approach on the ICDAR 2003 database and another database of scene images consisting of text of Indian scripts.

关键词： Image edge detection Image color analysis Transforms Databases Feature extraction Robustness pattern recognition

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F-ratio Based Weighted Feature Extraction for Similar Shape Character recognition

F-ratio Based Weighted Feature Extraction for Similar Shape ...

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International Conference on Document Analysis and recognition

作者： Tetsushi Wakabayashi Umapada Pal Fumitaka Kimura Yasuji Miyake Graduate School of Engineering Mie University Tsu Mie Japan Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424445004

recognition of handwritten similar shaped character is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. In this paper we proposed a novel feature extraction technique to improve the recognition results of two similar shaped characters. The technique is based on F-ratio (Fisher Ratio), a statistical measure defined by the ratio to the between-class variance and within-class variance. F-ratio modifies the feature vector of two similar shape characters by weighting the feature elements. This weighting scheme enhances the feature elements that belongs to the distinguishable portions of the similar shaped characters and reduces the feature elements of the common portion of the characters, so that similar shaped characters can be identified easily. We considered pair of handwritten similar shape characters of different scripts like Arabic/Persian, Devnagari English, Bangla, Oriya, Tamil, Kannada, Telugu etc. and we noted that f-ratio based feature weighting shows better recognition results.

关键词： Feature extraction Shape Character recognition Handwriting recognition pattern recognition Text analysis pattern analysis computer vision computer errors Writing

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A System for Off-Line Oriya Handwritten Character recognition Using Curvature Feature

A System for Off-Line Oriya Handwritten Character Recognitio...

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International Conference on Information Technology (ICIT)

作者： U. Pal T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Mie Japan

In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 49times49 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with pi/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 7times7 blocks from 49times49 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (7times7 blocks times 8 directions times 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system.

关键词： Character recognition Shape Handwriting recognition computer vision Spatial resolution Speech recognition Information technology pattern recognition Image segmentation Interpolation

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Rural cardiac healthcare system-A scheme for developing countries

Rural cardiac healthcare system-A scheme for developing coun...

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IEEE Region 10 International Conference TENCON

作者： Sucharita Mitra M. Mitra B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Applied Physics Faculty of Technology University of Calcutta India

A simple, easy to implement and low cost mobile health(m-health) solution using general packet radio service (GPRS) of globally extended cellular mobile network for economically deprived countries is described in this paper. According to different statistical surveys, heart disease is a major health burden throughout the world but more importantly the third world countries like India, Pakistan, Bangladesh. We consider here a telecardiology system, where the ECG images captured by mobile camera are sent using Multimedia Massage Service (MMS) to a data extraction and processing unit. This unit can extract digitized data from those images, which may be restored as database in a dedicated server and also analyzed by an inbuilt knowledgebase as well as a rough decision generating system. Ultimately, the system will generate an automated report that will suggest the primary treatment and preventive measures to be taken by the patient. The report will be sent back to the patient by Short Message Service (SMS) through the same mobile system. The main advantage of the system is that it will not require constant monitoring or presence of a doctor at the initial stage. The ECG of the patient with critical abnormality or unknown abnormality will be directly sent to the doctorpsilas mobile and intimation will also be sent to the patient site directly. The extreme popularity of mobile phone with camera in the third world countries makes this architecture practically realizable.

关键词： Medical services Electrocardiography Cameras Data mining Costs Packet radio networks Ground penetrating radar Cellular networks Cardiac disease Multimedia systems

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Signature Segmentation from Machine Printed Documents Using Conditional Random Field

Signature Segmentation from Machine Printed Documents Using ...

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International Conference on Document Analysis and recognition

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire dInformatique Université François Rabelais Tours France

ISBN: (纸本)9781457713507

Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset1 and we have obtained encouraging results.

关键词： Support vector machines Handwriting recognition Image segmentation Feature extraction Junctions Labeling Hidden Markov models

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Sclera recognition - A Survey

Sclera Recognition - A Survey

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Asian Conference on pattern recognition (ACPR)

作者： Abhijit Das Umapada Pal Michael Blumenstein Miguel Angel Ferrer Ballester Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue and blood vessels, surrounds the iris. A survey of the techniques available in the area of sclera biometrics will be of great assistance to researchers, and hence a comprehensive effort is made in this article to discuss the advancements reported in this regard during the past few decades. As a limited number of publications are found in the literature, an attempt is made in this paper to increase awareness of this area so that the topic gains popularity and interest among researchers. In this survey, a brief introduction is given initially about the sclera biometric, which is subsequently followed by background concepts, various pre-processing techniques, feature extraction and finally classification techniques associated with the sclera biometric. Benchmarking databases are very important for any pattern recognition related research, so the databases related with this work is also discussed. Finally, our observations, future scope and existing difficulties, which are unsolved in sclera biometrics, are discussed. We hope that this survey will serve to focus more researcher attention towards the emerging sclera biometric.

关键词： Iris recognition Biomedical imaging Image segmentation Feature extraction pattern recognition Image color analysis Image recognition

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