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Automatic separation of words in multi-lingual multi-script Indian documents

Automatic separation of words in multi-lingual multi-script ...

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International Conference on Document Analysis and recognition

作者： U. Pal B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

In a multi-lingual country like India, a document may contain more than one script forms. For such a document it is necessary to separate different script forms before feeding them to OCRs of individual script. In this paper an automatic word segmentation approach is described which can separate Roman, Bangla and Devnagari scripts present in a single document. The approach has a tree structure where at first Roman script words are separated using the 'headline' feature. The headline is common in Bangla and Devnagari but absent in Roman. Next, Bangla and Devnagari words are separated using some finer characteristics of the character set although recognition of individual character is avoided. At present, the system has an overall accuracy of 96.09%.

关键词： Optical character recognition software Natural languages computer vision Shape pattern recognition Tree data structures Character recognition Continents Cleaning Character generation

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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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International Conference on pattern recognition

作者： U. Garain B.B. Chaudhuri T.T. Pal Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)076951695X

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 was designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles. Initially, the approach was 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: 97.29% for Devnagari and 96.34% for Bangla.

关键词： Handwriting recognition Humans Character recognition pattern recognition Keyboards computer vision Writing Job design Feature extraction Algorithm design and analysis

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Automatic recognition of printed Oriya script

Automatic recognition of printed Oriya script

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International Conference on Document Analysis and recognition

作者： B.B. Chaudhuri U. Pal M. Mitra Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

ISBN: (纸本)0769512631

The paper deals with an optical character recognition system for printed Oriya, a popular Indian script. The development of OCR for this script is difficult because a large number of characters have to be recognized. In the proposed system, the digitized document image is first passed through preprocessing modules like skew correction, line segmentation, zone detection, word and character segmentation, etc. These modules have been developed by combining some conventional techniques with some newly proposed ones. Next, individual characters are recognized using a combination of stroke and run-number based features, along with features obtained from the concept of a water reservoir. The feature detection methods are simple and robust. A prototype of the system has been tested on a variety of printed Oriya material, and currently achieves 96.3% character level accuracy on average.

关键词： Character recognition Optical character recognition software Image segmentation Water resources Reservoirs computer vision Robustness Prototypes Materials testing System testing

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An approach for stemming in symbolically compressed Indian language imaged documents

An approach for stemming in symbolically compressed Indian l...

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International Conference on Document Analysis and recognition

作者： U. Garain A.K. Datta Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots, and thereby improving the overall retrieval efficiency. This paper presents an algorithm for stemming in the context of document image retrieval system. The algorithm assumes that the documents are symbolically compressed and stemming has been attempted in the compressed domain itself. Experiments have been conducted on Indian language imaged documents for which efficient OCR still remains a challenging task. Results obtained from a set 150 document images (in Bangla script, the second most popular script in the Indian sub-continent) consisting of about 12K word show a promising performance of the proposed approach.

关键词： Image coding Image retrieval Optical character recognition software Information retrieval Internet Image storage computer vision pattern recognition Search engines Character recognition

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Script line separation from Indian multi-script documents

Script line separation from Indian multi-script documents

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International Conference on Document Analysis and recognition

作者： U. Pal B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other official Indian languages. For OCR of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper, an automatic technique of separating the text lines using script characteristics and shape based features is presented. At present, the system has an overall accuracy of about 98.5%.

关键词： Natural languages Optical character recognition software Shape Optical filters computer vision pattern recognition Writing Read only memory Character generation

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Automatic identification of English, Chinese, Arabic, Devnagari and Bangla script line

Automatic identification of English, Chinese, Arabic, Devnag...

引用

International Conference on Document Analysis and recognition

作者： U. Pal B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

In a general situation, a document page may contain several scriptforms. For optical character recognition (OCR) of such a document page, it is necessary to separate the scripts before feeding them to their individual OCR systems. An automatic technique for the identification of printed Roman, Chinese, Arabic, Devnagari and Bangla text lines from a single document is proposed. Shape based features, statistical features and some features obtained from the concept of a water reservoir are used for script identification. The proposed scheme has an accuracy of about 97.33%.

关键词： Water resources Reservoirs Optical character recognition software Shape Water storage Probability computer vision pattern recognition Optical devices Fractals

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A New Defect Detection Method for Improving Text Detection and recognition Performances in Natural Scene Images

A New Defect Detection Method for Improving Text Detection a...

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2020 Swedish Workshop on Data Science, SweDS 2020

作者： Mokayed, Hamam Shivakumara, Palaiahnakote Liwicki, Marcus Pal, Umapada University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Lulea University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781728192048

This paper presents a new idea for improving text detection and recognition performances by detecting defects in the text detection results. Despite the rapid development of powerful deep learning based models for scene text detection and recognition in the wild, in complex situations (logos or decorated components connected with text), existing methods do not yield satisfactory results. In this paper, we propose to use post-processing method to improve the text detection and recognition performance. The proposed method extracts features, namely phase congruency, entropy and compactness for the text detection results. To strengthen discriminative power for feature extraction, we explore the combination of SVM classifier and Gaussian distribution of text components to determine proper weight, which represents true text component. The weights are multiplied with the features to detect defect components though clustering. The bounding boxes are redrawn, which results proper bounding box without defects components. Experimental results show that the proposed defect detection reports satisfactory results. To validate the effectiveness of defect detection, we conduct experiments on benchmark datasets of MSRA-TD-500 and SVT for detection and recognition before and after defect detection. The result shows that the performance of text detection and recognition improves significantly after defect detection. © 2020 IEEE.

关键词： Gaussian distribution

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Convex hull based approach for multi-oriented character recognition from graphical documents

Convex hull based approach for multi-oriented character reco...

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作者： Roy, Partha Pratim Pal, Umapada Lladós, Josep Kimura, Fumitaka Computer Vision Center Universitat Autònoma De Barcelona 08193 Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata - 108 India Graduate School of Engineering Mie University 1577 Kurimamachiya Mie 514-8504 Japan

ISBN: (纸本)9781424421756

In this paper, we present a scheme towards recognition of English character in multi-scale and multi-oriented environments. Graphical document such as map consists of text lines which appear in different orientation. Sometimes, characters in a single word may follow a curvi-linear way to annotate the graphical curve lines. For recognition of such multi-scale and multi-oriented characters a Support Vector Machine (SVM) based scheme is presented in this paper. The feature used here is invariant to character orientation. Circular ring and convex hull have been used along with angular information of the contour pixels of the character to make the feature rotation invariant. We tested our proposed scheme on two different datasets. Combining circular and convex hull feature we have obtained 96.73% and 99.56% accuracy in these two datasets. © 2008 IEEE.

关键词： Support vector machines

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Using text corpora for understanding polysemy in Bangla

Using text corpora for understanding polysemy in Bangla

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Language Engineering Conference

作者： N.S. Dash B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Polysemy implies the presence of more than one sense of a particular word both in its context-bound and context-free situation. The inherent aspect of polysemy is that a particular word will show multiple sense variations related by way of semantic extension and conceptual expansion. In the last fifty years or so, polysemy has been recognised as one of the central issues in lexical semantics, word sense disambiguation (WSD), actual sense extraction (ASE), language learning, conceptual categorisation of words as well as in computer processing of language. Language users can identify a polysemous word quite easily, but are not equipped to decipher all its possible sense variations without appropriate reference to a proper knowledge-base and other relevant information embedded within contextual environments. We make an empirical effort to understand the basic nature of polysemy in Bangla. We also intend to know how words denote sense variations, which factors are instrumental in making them polysemous, what impact they have on language understanding, and how sense variation can be best understood using information from various sources of knowledge bases. Finally, we use a method to understand the role of various contexts, obtained from corpora, in maintaining an interface between words and their sense variations. Quick reference to local context is handy at times, but reference to focal, topical and global contexts as well as an extralinguistic knowledge base is necessary for understanding sense variation and obtaining actual contextual sense.

关键词： Psychology Computational linguistics computer vision pattern recognition Data mining Instruments Artificial intelligence Information analysis Humans Cognition

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An End-to-End System for Bangla Online Handwriting recognition

An End-to-End System for Bangla Online Handwriting Recogniti...

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International Workshop on Frontiers in Handwriting recognition

作者： Soumik Bhattacharya Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781509009824

A few studies of online Bangla handwriting recognition such as isolated character recognition or limited vocabulary cursive word recognition are found in the literature. However, development of an end-to-end recognition system of unconstrained online Bangla handwritten texts has not been duly attempted so far. In the present report, we describe a similar system which takes a piece of continuous online handwritten Bangla texts as the input. It first segments the input texts into individual lines, each line into its constituent words and each word into sub-strokes. In the present study, 152 different symbols which include basic characters, character modifiers, frequently used conjunct characters, a few special characters and numerals have been considered. The entire set of sub-strokes obtained from the training sample set has been exhaustively studied by 3 experts and 76 different shapes of sub-strokes have been identified based on consensus among these experts. Also, it has been observed that a character may produce at most 3 sub-strokes. Since a piece of Bangla texts often contains either Bangla or English numerals, the present character set consists of both the numeral set and 3 numeral shapes are common to both the scripts. The proposed recognition system uses two classifiers, one for characters and the other for sub-strokes. Sub-strokes are fed to the character classifier in their temporal order. A single sub-stroke followed by two consecutive sub-strokes and finally three successive sub-strokes are passed to the character classifier and the first two top responses of the character classifier among the three cases are compared. If the difference is less than a threshold, the response of sub-stroke classifier is used to reach a final decision. The proposed system provided 94.3% character level accuracy on a test set consisting of 33,453 word samples written by 31 writers.

关键词： Handwriting recognition Character recognition Shape Text recognition Support vector machines Vocabulary Trajectory

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