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Segmentation of touching symbols for OCR of printed mathematical expressions: an approach based on multifactorial analysis

Segmentation of touching symbols for OCR of printed mathemat...

引用

International Conference on Document Analysis and recognition

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

This paper deals with segmentation and recognition of touching characters appearing in scanned mathematical expressions. The technique is based on multifactorial analysis that integrates several factors determining cut-positions in a touching character image. A predictive algorithm is developed for efficient selection of possible cut-positions for segmenting touching characters. Experiment has been carried out using a test-set of reasonable size and results show that a considerable improvement in recognition accuracy can be achieved with a modest increase in computations.

关键词： Optical character recognition software Image segmentation Character recognition pattern recognition Image analysis Image databases pattern analysis computer vision Prediction algorithms Testing

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Script line separation from Indian multi-script documents

Script line separation from Indian multi-script documents

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 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 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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OCR error detection and correction of an inflectional Indian language script

OCR error detection and correction of an inflectional Indian...

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International Conference on pattern recognition

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

This paper deals with an OCR error detection and correction technique for a highly inflectional language script like Bangla (a major Indian language). This is the first report of its kind. Using two separate lexicons of root words and suffixes, candidate root-suffix pairs of each input word are detected, their grammatical agreement are tested and the root/suffix part in which the error has occurred is noted. The correction is made on the corresponding error part of the input string by a fast dictionary access technique. To do so some alternative strings are generated for an erroneous word. Among the alternative strings, those satisfying grammatical agreement in root-suffix and also having smallest Levenstein-Damerau distance are finally chosen as the correct ones. The system has an accuracy of 75.61%.

关键词： Optical character recognition software Error correction Dictionaries computer errors Character recognition computer vision pattern recognition Testing Libraries Optical design

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Neural network based word-wise handwritten script identification system for Indian postal automation

Neural network based word-wise handwritten script identifica...

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International Conference on Intelligent Sensing and Information Processing (ICISIP)

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

Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for Indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for Indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piecewise projection method the destination address block (DAB), is at first segmented into lines and then into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, fractal based feature, etc. a neural network (NN) classifier is generated for word-wise Bangla and English scripts identification. Overall accuracy of the proposed system is at present 9 7.62%.

关键词： Neural networks Automation Natural languages Water resources Reservoirs Fractals Optical character recognition software computer vision pattern recognition USA Councils

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An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi)

An OCR system to read two Indian language scripts: Bangla an...

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International Conference on Document Analysis and recognition

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

An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent. These scripts, having the same origin in ancient Brahmi script, have many features in common and hence a single system can be modeled to recognize them. In the proposed model, document digitization, skew detection, text line segmentation and zone separation, word and character segmentation, character grouping into basic, modifier and compound character category are done for both scripts by the same set of algorithms. The feature sets and classification tree as well as the knowledge base required for error correction (such as lexicon) differ for Bangla and Devnagari. The system shows a good performance for single font scripts printed on clear documents.

关键词： Optical character recognition software Natural languages Error correction Character recognition Switches computer vision pattern recognition Cleaning Text recognition Writing

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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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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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Multi-skew detection of Indian script documents

Multi-skew detection of Indian script documents

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International Conference on Document Analysis and recognition

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

ISBN: (纸本)0769512631

There are many documents where text lines are not parallel to each other i.e. these lines have different inclinations with the horizontal lines (multi-skew documents). For the OCR of such a document we have to estimate the skew angle of individual text lines because a single rotation cannot de-skew all text lines of the document. In this paper, we describe a robust technique for multi-skew angle detection from Indian documents containing the most popular Indian scripts Devnagari and Bangla. Most characters in these scripts have horizontal lines at the top, called head-lines. The character head-lines usually connect one another in a word and the word appears as a single component. In the proposed method, the connected components are at first labeled and selected. The upper envelopes of selected components are found by column-wise scanning from the top of the component. Portions of the upper envelope satisfying the properties of a digital straight line are detected. They are then clustered into groups belonging to single text lines. Estimates from these individual clusters give the skew angle of each text line. The proposed multi-skew detection technique has an accuracy about 98.3%.

关键词： Strips Fourier transforms Optical character recognition software Robustness computer vision pattern recognition Envelope detectors Humans Goniometers Gray-scale

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Wavelet-based approach for writer identification in music score

Wavelet-based approach for writer identification in music sc...

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IEEE International Advance Computing Conference, IACC

作者： Debleena Das Sukalpa Chanda Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479925735

Writer identification is a vibrant research field, though a lot of work has been done on writer identification on normal text, writer identification in music score sheet has not been addressed in that large scale. Here we propose a method to identify writers of music score sheets using Daubchies wavelet features along with SVM classifier. We have evaluated our proposed approach in a sub-set of CVC-MUSCIMA dataset. From the experiment on 140 score sheet images from 7 writers we obtained encouraging results.

关键词： Support vector machines Feature extraction Accuracy Conferences Training Wavelet transforms

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