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Word-wise Thai and Roman script identification

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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Shape code based word-image matching for retrieval of Indian multi-lingual documents

Shape code based word-image matching for retrieval of Indian...

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International Conference on pattern recognition

作者： Tarafdar, Arundhati Mondal, Ranju Pal, Srikanta Pal, Umapada Kimura, Fumitaka Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India Graduate School of Engineering Mie University Kurimamachiya-cho Tsu Japan

ISBN: (纸本)9780769541099

In the current scenario retrieving information from document images is a challenging problem. In this paper we propose a shape code based word-image matching (words-potting) technique for retrieval of multilingual documents written in Indian languages. Here, each query word image to be searched is represented by a primitive shape code using (i) zonal information of extreme points (ii) vertical shape based feature (iii) crossing count (with respect to vertical bar position) (iv) loop shape and position (v) background information etc. Each candidate word (a word having similar aspect ratio and topological feature to the query word) of the document is also coded accordingly. Then, an inexact string matching technique is used to measure the similarity between the primitive codes generated from the query word image and each candidate word of the document with which the query image is to be searched. Based on the similarity score, we retrieve the document where the query image is found. Experimental results on Bangla, Devnagari and Gurumukhi scripts document image databases confirm the feasibility and efficiency of our proposed approach. © 2010 IEEE.

关键词： Image matching

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Text line identification in Tagore's manuscript

Text line identification in Tagore's manuscript

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2014 3rd IEEE Students' Technology Symposium, IEEE TechSym 2014

作者： Adak, Chandranath Chaudhuri, Bidyut B. Dept. of Computer Science and Engg. University of Kalyani West-Bengal-741235 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9781479926084

In this paper, a text line identification method is proposed. The text lines of printed document are easy to segment due to uniform straightness of the lines and sufficient gap between the lines. But in handwritten documents, the line is nonuniform and interline gaps are variable. We take Rabindranath Tagore's manuscript as it is one of the most difficult manuscripts that contain doodles. Our method consists of a preprocessing stage to clean the document image. Then we separate doodles from the manuscript to get the textual region. After that we identify the text lines on the manuscript. For text line identification, we use window examination, black run-length smearing, horizontal histogram and connected component analysis. © 2014 IEEE.

关键词： document image processing handwritten character recognition image segmentation optical character recognition text analysis Rabindranath Tagore manuscript black run-length smearing connected component analysis document image cleaning doodle separation handwritten documents horizontal histogram nonuniform line preprocessing stage printed document text line identification method text line segmentation textual region uniform line straightness variable interline gaps window examination Character recognition Frequency modulation Handwriting recognition Histograms Image analysis Optical filters Text analysis document image analysis doodle handwritten document manuscript processing text line identification

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Off-line Bangla signature verification

Off-line Bangla signature verification

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Pal, Srikanta Nguyen, Vu Blumenstein, Michael Pal, Umapada School of Information and Communication Technology Griffith University Gold Coast QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9780769546612

In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments. © 2012 IEEE.

关键词： Biometrics

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Two-stage recognition of handwritten bangla alphanumeric characters using neural classifiers

Two-stage recognition of handwritten bangla alphanumeric cha...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Parui, S.K. Sridhar, M. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Electrical and Computer Engineering University of Michigan-Dearborn United States Faculty of Engineering Mie University Japan

ISBN: (纸本)0972741216

No significant research work towards recognition of handwritten Bangla characters has yet been done. Only a few works in this area are found in the literature which are based on small databases col-lected in laboratory environments. Recently, a large and representative database of Bangla character images [1] has been developed. The first part of this article is devoted to shape analysis of samples of this database vis-a-vis shapes of the respective printed characters. The next part describes a two-stage scheme for recognition of handwritten Bangla alphanumeric characters. This is a 57 class problem (shapes of 3 numerals are not distinguishable from shapes of 3 basic characters among 10 numerals and 50 basic characters). In the first stage of this recognition scheme, an input character is identified in one of 11 smaller groups of characters. In the second stage, classification is performed within the particular smaller group as determined by the first stage. In both the stages of the proposed approach, local chain code histograms of character skeleton are used as the feature vector and multilayer perceptron (MLP) as the classifier. recognition accuracies obtained by the proposed approach on the training and test sets of the present handwritten Bangla alphanumeric character database are respectively 91.45% and 84.65%. Copyright © IICAI 2005.

关键词： Database systems

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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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A New Contrastive Learning Based Model for Estimating Degree of Multiple Personality Traits Using Social Media Posts 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Biswas, Kunal Shivakumara, Palaiahnakote Pal, Umapada Sarkar, Ram Jadavpur University Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that when the image contains multiple persons and modalities, one can expect multiple emotions and expressions. This work separates given input images into different faces of people, recognized text, meta-text and background information using face segmentation, text recognition and scene detection techniques. Contrastive learning is explored to extract features from each segmented region based on clustering. The proposed work fuses textual and visual features extracted from the image for estimating the degree of multiple personality traits. Experimental results on our benchmark datasets show that the proposed model is effective and outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image segmentation

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Online handwritten Indian script recognition: A human motor function based framework

Online handwritten Indian script recognition: A human motor ...

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作者： Garain, U. Chaudhuri, B.B. Pal, T.T. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata 700 108 India

This paper presents the online handwriting recognition for Indian scripts. The primary concern of the approach is the modeling of human motor functionality while writing characters. This is achieved by looking at the whole pen trajectory where the time evaluation of the pen coordinates plays a crucial role. A low complexity classifier has been designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles. Initially, the approach has been applied for online recognition of handwritten characters in Devnagari and Bangla, the two major Indian scripts. A test on a dataset of considerable size shows promising recognition rates namely, 97.29% for Devnagari and 96.34% for Bangla. © 2002 IEEE.

关键词： pattern recognition

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Bangla handwritten character recognition

Bangla handwritten character recognition

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Roy, K. Pal, U. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Faculty of Engineering Mie University 1577 Kurimamachiya-cho Mie 514-8504 Japan

ISBN: (纸本)0972741216

recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten characters using quadratic classifier based on the features obtained from directional chain code histogram. Here the bounding box of a character is segmented into blocks and chain code histogram is computed in each of the blocks. This chain code features are fed to the quadratic classifier for recognition. We tested the result on 14879 characters and we obtained 90.53% accuracy with 0.81% rejection and 8.66% error, when 50 classes are considered. From 35 classes we obtained 93.90% accuracy with 0.91% rejection and 5.19% error rate. Copyright © IICAI 2005.

关键词： Graphic methods

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Handwritten bangla compound character recognition using gradient feature

Handwritten bangla compound character recognition using grad...

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10th International Conference on Information Technology, ICIT 2007

ISBN: (纸本)0769530680

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 X 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. © 2007 IEEE.

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