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Text independent writer identification for Bengali script

Text independent writer identification for Bengali script

引用

International Conference on pattern recognition

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

ISBN: (纸本)9780769541099

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. © 2010 IEEE.

关键词： Support vector machines

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Script identification - A Han & Roman script perspective

Script identification - A Han & Roman script perspective

引用

2010 20th International Conference on pattern recognition, ICPR 2010

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

ISBN: (纸本)9780769541099

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. © 2010 IEEE.

关键词： Optical character recognition

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Word-wise handwritten Persian and Roman script identification

Word-wise handwritten Persian and Roman script identificatio...

引用

International Conference on Frontiers in Handwriting recognition

作者： Roy, Kaushik Alaei, Alireza Pal, Umapada Department of Computer Science West Bengal State University Kolkata-126 India 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)9780769542218

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. © 2010 IEEE.

关键词： Fractal dimension

来源：评论

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Document-Zone Classification in Torn Documents

Document-Zone Classification in Torn Documents

引用

International Workshop on Frontiers in Handwriting recognition

作者： Sukalpa Chanda Katrin Franke Umapada Pal Department of Computer Science and Media Technology Gjovik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Arbitrary orientation and sparse data content are common characteristics of torn document. To ensure accuracy and reliability in computer-based analysis, content-zone segmentation is required. In our previous work, we studied segmentation of handwritten and printed text. A questioned document-piece in the form of an office note, however, might also contain non-text data like logos, graphics, and pictures. Hence a more precise content-zone classification is required. In this paper we propose a two-tier approach for non-text, handwriting and printed text segmentation. The first tier aims to discriminate text and non-text regions. The second tier classifies handwritten and printed text within all text zones identified during the first tier. Gabor features and chain-code features are used in Tier-1 and Tier-2, respectively. By using SVM classifier we successfully identified 97.65% of 31,227 text regions in our current test data. The proposed approach identified 98.69% of printed and 96.39% of handwritten text amongst all identified text regions.

关键词： Accuracy Graphics Gabor filters Support vector machines Pixel Feature extraction Image segmentation

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A New Two-Stage Scheme for the recognition of Persian Handwritten Characters

A New Two-Stage Scheme for the Recognition of Persian Handwr...

引用

International Workshop on Frontiers in Handwriting recognition

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

In this paper, a two-stage scheme for the recognition of Persian handwritten isolated characters is proposed. In the first stage, similar shaped characters are categorized into groups and as a result, 8 groups are obtained from 32 Persian basic characters. In the second stage, the groups containing more than one similar shape characters are considered further for the final recognition. Feature extraction is based on under sampled bitmaps technique and modified chain-code direction frequencies. For the first stage features, we compute 49-dimension features based on under sampled bitmaps from 49 non-overlapping 7 × 7 window-maps. 196-dimension chain-code direction frequencies from 49 overlapping 9 × 9 window-maps are computed and used as features for the second stage of the proposed scheme. Classifiers are one-against-other support vector machines (SVM). We evaluated our scheme on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested our scheme on other 15338 samples and obtained 98.10% and 96.68% correct recognition rates when considered 8-class and 32-class problems, respectively.

关键词： Character recognition Feature extraction Shape Support vector machines Accuracy Handwriting recognition Pixel

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A Baseline Dependent Approach for Persian Handwritten Character Segmentation

A Baseline Dependent Approach for Persian Handwritten Charac...

引用

International Conference on pattern recognition

ISBN: (纸本)9781424475421

In this paper, an efficient approach to segment Persian off-line handwritten text-line into characters is presented. The proposed algorithm first traces the baseline of the input text-line image and straightens it. Subsequently, it over-segments each word/subwords using features extracted from histogram analysis and then removes extra segmentation points using some baseline dependent as well as language dependent rules. We tested the proposed character segmentation scheme with 2 different datasets. On a test set of 899 Persian words/subwords created by us, 90.26% of the characters were segmented correctly. From another dataset of 200 handwritten Arabic word images we obtained 93.49% correct segmentation accuracy.

关键词： Image segmentation Handwriting recognition Shape Algorithm design and analysis Equations Smoothing methods Feature extraction

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Color Feature Based Approach for Determining Ink Age in Printed Documents

Color Feature Based Approach for Determining Ink Age in Prin...

引用

International Conference on pattern recognition

作者： Biswajit Halder Utpal Garain Department of Information Technology Mallabhum Institute of Technology Bisnupur West Bengal India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE magazines published in between 1930's and 70's (five decades). Test results show that a viable framework for involving machines in assisting human experts for determining age of printed documents.

关键词： Ink Image color analysis Feature extraction Pixel Forensics Artificial neural networks Accuracy

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Word-Wise Handwritten Persian and Roman Script Identification

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

引用

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

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

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Text Independent Writer Identification for Bengali Script

Text Independent Writer Identification for Bengali Script

引用

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

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

引用

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

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

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