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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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A system for word-wise handwritten script identification for indian postal automation

A system for word-wise handwritten script identification for...

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IEEE India Conference (INDICON)

作者： K. Roy A. Banerjee U. Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute 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 piece-wise projection method the destination address block (DAB) is at first segmented into lines and then links into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, etc. a tree classifier is generated for word-wise Bangla/Devnagari and English scripts identification.

关键词： Automation Natural languages Water resources Reservoirs Optical character recognition software Australia Smoothing methods Seals Histograms Feature extraction

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CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification

CoCoNet: A Collaborative Convolutional Network applied to fi...

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International Conference on Image and vision Computing New Zealand, IVCNZ

作者： Tapabrata Chakraborti Brendan McCane Steven Mills Umapada Pal University of Otago Computer Vision and Pattern recognition Unit Indian Statistical Institute

The following topics are dealt with: convolutional neural nets; learning (artificial intelligence); image classification; computer vision; feature extraction; video signal processing; deep learning (artificial intelli... 详细信息

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

关键词： Training Visualization Image recognition Collaboration computer architecture Birds Task analysis

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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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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...

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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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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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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 syntactic approach for processing mathematical expressions in printed documents

A syntactic approach for processing mathematical expressions...

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International Conference on pattern recognition

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

ISBN: (纸本)0769507506

We propose an approach for understanding mathematical expressions in printed documents. The overall approach is divided into three main steps: (i) detection of mathematical expressions in a document, (ii) recognition of the symbols present in the expression and (iii) arrangement of the recognized symbols. The detection of mathematical expressions is done through recognition of a few most common symbols and exploiting some structural features of the expressions. A hybrid of feature based and a template-based technique is used for the recognition of symbols. A two-pass approach is used for arrangement of the symbols. The first pass (scanning or lexical analysis) performs a micro-level examination of the symbols in order to identify the symbol groups occurring in them and to determine their categories or descriptors. The second pass (parsing or syntax analysis) processes the descriptors synthesized in the first pass, to determine the syntactic structure of the expression. A set of predefined rules guides the activities in both the passes. Experiments conducted using this approach on a large number of documents show high accuracy.

关键词： Optical character recognition software computer vision pattern recognition Performance analysis Equations Document handling Testing Books White spaces

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A CNN Based Framework for Unistroke Numeral recognition in Air-Writing

arXiv

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arXiv 2023年

作者： Roy, Prasun Ghosh, Subhankar Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Air-writing refers to virtually writing linguistic characters through hand gestures in three-dimensional space with six degrees of freedom. This paper proposes a generic video camera-aided convolutional neural network (CNN) based air-writing framework. Gestures are performed using a marker of fixed color in front of a generic video camera, followed by color-based segmentation to identify the marker and track the trajectory of the marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies significantly on the illumination condition due to color-based segmentation. In a less fluctuating illumination condition, the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework has achieved 97.7%, 95.4% and 93.7% recognition rates in person independent evaluations on English, Bengali and Devanagari numerals, respectively. © 2023, CC BY.

关键词： Convolutional neural networks

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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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