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International Conference on pattern recognition

作者： Sukalpa Chanda Katrin Franke Umapada Pal Tetsushi Wakabayashi Department of Computer Science and Media Technology Gjovik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781424475421

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be always ensured. Also, compromising in terms of systems reliability under such situation is not desirable. We here propose a system to encounter such adverse situation in the context of Bengali script. Experiments with discrete directional feature and gradient feature are reported here, along with Support Vector Machine (SVM) as classifier. We got promising results of 95.19% writer identification accuracy at first top choice and 99.03% when considering first three top choices.

关键词： Accuracy Support vector machines Training Handwriting recognition Forensics Text analysis

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Script Identification – A Han and Roman Script Perspective

Script Identification – A Han and Roman Script Perspective

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International Conference on pattern recognition

作者： Sukalpa Chanda Umapada Pal Katrin Franke Fumitaka Kimura Department of Computer Science and Media Technology Gjovik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

All Han-based scripts (Chinese, Japanese, and Korean) possess similar visual characteristics. Hence system development for identification of Chinese, Japanese and Korean scripts from a single document page is quite challenging. It is noted that a Han-based document page might also have Roman script in them. A multi-script OCR system dealing with Chinese, Japanese, Korean, and Roman scripts, demands identification of scripts before execution of respective OCR modules. We propose a system to address this problem using directional features along with a Gaussian Kernel-based Support Vector Machine. We got promising results of 98.39% script identification accuracy at character level and 99.85% at block level, when no rejection was considered.

关键词： Accuracy Support vector machines Training Kernel Feature extraction Image segmentation Optical character recognition software

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Bangla and English City Name recognition for Indian Postal Automation

Bangla and English City Name Recognition for Indian Postal A...

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International Conference on pattern recognition

作者： Umapada Pal Ramit Kumar Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Saint Xavier's College Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781424475421

Because of multi-lingual behavior destination address block of a postal document of an Indian state may be written in two or more scripts. From a statistical analysis of Indian postal document we noted that about 22.04% of Indian postal documents are written in two scripts. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such identification difficulties, in this paper we proposed a lexicon-driven bi-lingual (English and Bangla) city name recognition scheme for Indian postal automation. We obtained 93.19% accuracy when tested on 11875 city name samples.

关键词： Cities and towns Handwriting recognition Feature extraction Automation Image segmentation Cavity resonators Dynamic programming

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Word-Wise Handwritten Persian and Roman Script Identification

Word-Wise Handwritten Persian and Roman Script Identificatio...

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International Workshop on Frontiers in Handwriting recognition

作者： Kaushik Roy Alireza Alaei Umapada Pal Department of Computer Science West Bengal State University Kolkata India Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification from bi-script documents written in Persian and Roman. In the proposed scheme, simple but fast computable set of 12 features based on fractal dimension, position of small component, topology etc. are used and a set of classifiers are employed for script identification experiments. We tested our scheme on a dataset of 5000 handwritten Persian and English words and 99.20% of correct script identification is obtained.

关键词： Fractals Support vector machines Kernel Polynomials Training Artificial neural networks Neurons

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Devanagari and Bangla text extraction from natural scene images

Devanagari and Bangla text extraction from natural scene ima...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Bhattacharya, U. Parui, S.K. Mondal, S. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata - 108 India

ISBN: (纸本)9780769537252

With the increasing popularity of digital cameras attached with various handheld devices, many new computational challenges have gained significance. One such problem is extraction of texts from natural scene images captured by such devices. The extracted text can be sent to OCR or to a text-to-speech engine for recognition. In this article, we propose a novel and effective scheme based on analysis of connected components for extraction of Devanagari and Bangla texts from camera captured scene images. A common unique feature of these two scripts is the presence of headline and the proposed scheme uses mathematical morphology operations for their extraction. Additionally, we consider a few criteria for robust filtering of text components from such scene images. Moreover, we studied the problem of binarization of such scene images and observed that there are situations when repeated binarization by a well-known global thresholding approach is effective. We tested our algorithm on a repository of 100 scene images containing texts of Devanagari and / or Bangla © 2009 IEEE.

关键词： Extraction

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A complete system for detection and recognition of text in graphical documents using background information

A complete system for detection and recognition of text in g...

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4th International Conference on computer vision Theory and Applications, VISAPP 2009

作者： Roy, Partha Pratim Lladòs, Josep Pal, Umapada Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata - 108 India

ISBN: (纸本)9789898111692

Automatic Text/symbols retrieval in graphical documents (map, engineering drawing) involves many challenges because they are not usually parallel to each other. They are multi-oriented and curve in nature to annotate the graphical curve lines and hence follow a curvi-linear way too. Sometimes, text and symbols frequently touch/overlap with graphical components (river, street, border line) which enhances the problem. For OCR of such documents we need to extract individual text lines and their corresponding words/characters. In this paper, we propose a methodology to extract individual text lines and an approach for recognition of the extracted text characters from such complex graphical documents. The methodology is based on the foreground and background information of the text components. To take care of background information, water reservoir concept and convex hull have been used. For recognition of multi-font, multi-scale and multi-oriented characters, Support Vector Machine (SVM) based classifier is applied. Circular ring and convex hull have been used along with angular information of the contour pixels of the characters to make the feature rotation and scale invariant.

关键词： Optical character recognition

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Machine authentication of security documents

Machine authentication of security documents

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit India Statistical Institute 203 B.T. Road Kolkata 700108 India WB India

ISBN: (纸本)9780769537252

This paper presents a pioneering effort towards machine authentication of security documents like bank cheques, legal deeds, certificates, etc. that fall under the same class as far as security is concerned. The proposed method first computationally extracts the security features from the document images and then the notion of 'genuine' vs. 'duplicate' is defined in the feature space. Bank cheques are taken as a reference for conducting the present experiment. Support Vector Machines (SVMs) and Neural Networks (NN) are involved to verify authenticity of these cheques. Results on a test dataset of 200 samples show that the proposed approach achieves about 98% accuracy for discriminating duplicate cheques from genuine ones. This strongly attests the viability of involving machine in authenticating security documents. © 2009 IEEE.

关键词： Support vector machines

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Fine classification of unconstrained handwritten Persian/Arabic numerals by removing confusion amongst similar classes

Fine classification of unconstrained handwritten Persian/Ara...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Alaei, Alireza Nagabhushan, P. Pal, Umapada Department of Studies in Computer Science University of Mysore Mysore 570 006 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9780769537252

In this paper, we propose two types of feature sets based on modified chain-code direction frequencies in the contour pixels of input image and modified transition features (horizontally and vertically). A multi-level support vector machine (SVM) is proposed as classifier to recognize Persian isolated digits. In first level, we combine similar shaped numerals into a single group and as result;we obtain 7 classes instead of 10 classes. We compute 196-dimension chain-code direction frequencies as features to discriminate 7 classes. In the second level, classes containing more than one numeral because of high resemblance in their shapes are considered. We use modified transition features (horizontally and vertically) for discriminating between two overlapping classes (0 and 1). To separate another overlapping group containing three numerals 2, 3 and 4 we first eliminate common parts of these digits (tail) and then compute chain code features. We employ SVM classifier for the classification and evaluate our scheme on 80,000 handwritten samples of Persian numerals [10]. Using 60,000 samples for training, we tested our scheme on other 20,000 samples and obtained 99.02% accuracy. © 2009 IEEE.

关键词： Support vector machines

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Word-wise Thai and Roman script identification

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ACM Transactions on Asian Language Information Processing 2009年第3期8卷 1–21页

作者： Chanda, Sukalpa Pal, Umapada Terrades, Oriol Ramos Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkatta-700108 India Instituto Tecnologico de Informatica Univ. Politécnica de Valencia 46022 Valencia Spain

In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. © 2009 ACM.

关键词： Optical character recognition

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Comparative study of Devnagari handwritten character recognition using different feature and classifiers

Comparative study of Devnagari handwritten character recogni...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University TSU Mie 514-8507 Japan

ISBN: (纸本)9780769537252

In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images. © 2009 IEEE.

关键词： Character recognition

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