Recognition of Handprinted Bangla Numerals Using Neural Network Models
Identifieur interne : 001C81 ( Istex/Corpus ); précédent : 001C80; suivant : 001C82Recognition of Handprinted Bangla Numerals Using Neural Network Models
Auteurs : U. Bhattacharya ; K. Das ; A. Datta ; K. Parui ; B. ChaudhuriSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: This paper proposes an automatic recognition scheme for handprinted Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.
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DOI: 10.1007/3-540-45631-7_31
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A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>U. Bhattacharya</name><affiliations><json:string>Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B. T. 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Road</Street><Postcode>700108</Postcode><City>Calcutta</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Brain Science Institute, RIKEN</OrgName><OrgAddress><Street>2-1 Hirosawa</Street><City>Wako</City><Country>Japan</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part3"><PartInfo TocLevels="0"><PartID>3</PartID><PartSequenceNumber>3</PartSequenceNumber><PartTitle>Neural Networks</PartTitle><PartChapterCount>6</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookID>3-540-45631-7</BookID><BookTitle>Advances in Soft Computing — AFSS 2002</BookTitle></PartContext></PartInfo><Chapter ID="Chap31" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>31</ChapterID><ChapterDOI>10.1007/3-540-45631-7_31</ChapterDOI><ChapterSequenceNumber>31</ChapterSequenceNumber><ChapterTitle Language="En">Recognition of Handprinted Bangla Numerals Using Neural Network Models</ChapterTitle><ChapterFirstPage>228</ChapterFirstPage><ChapterLastPage>235</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2002</CopyrightYear></ChapterCopyright><ChapterHistory><RegistrationDate><Year>2002</Year><Month>1</Month><Day>28</Day></RegistrationDate><OnlineDate><Year>2002</Year><Month>1</Month><Day>29</Day></OnlineDate></ChapterHistory><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>3</PartID><BookID>3-540-45631-7</BookID><BookTitle>Advances in Soft Computing — AFSS 2002</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>U.</GivenName><FamilyName>Bhattacharya</FamilyName></AuthorName><Contact><Email>ujjwal@isical.ac.in</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>T.</GivenName><GivenName>K.</GivenName><FamilyName>Das</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff6"><AuthorName DisplayOrder="Western"><GivenName>A.</GivenName><FamilyName>Datta</FamilyName></AuthorName><Contact><Email>amitava@isical.ac.in</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>S.</GivenName><GivenName>K.</GivenName><FamilyName>Parui</FamilyName></AuthorName><Contact><Email>swapan@isical.ac.in</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>B.</GivenName><GivenName>B.</GivenName><FamilyName>Chaudhuri</FamilyName></AuthorName><Contact><Email>bbc@isical.ac.in</Email></Contact></Author><Affiliation ID="Aff5"><OrgDivision>Computer Vision and Pattern Recognition Unit</OrgDivision><OrgName>Indian Statistical Institute</OrgName><OrgAddress><Street>203, B. T. Road</Street><Postcode>35</Postcode><City>Calcutta</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgDivision>Computer and Statistical Service Center</OrgDivision><OrgName>Indian Statistical Institute</OrgName><OrgAddress><Street>203, B. T. Road</Street><Postcode>35</Postcode><City>Calcutta</City><Country>India</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This paper proposes an automatic recognition scheme for handprinted Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle><SubSeriesSubTitle Language="En">Subseries of Lecture Notes in Computer Science</SubSeriesSubTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation ID="Aff3"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Recognition of Handprinted Bangla Numerals Using Neural Network Models</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Recognition of Handprinted Bangla Numerals Using Neural Network Models</title></titleInfo><name type="personal"><namePart type="given">U.</namePart><namePart type="family">Bhattacharya</namePart><affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B. T. Road, 35, Calcutta, India</affiliation><affiliation>E-mail: ujjwal@isical.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">T.</namePart><namePart type="given">K.</namePart><namePart type="family">Das</namePart><affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B. T. Road, 35, Calcutta, India</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">A.</namePart><namePart type="family">Datta</namePart><affiliation>Computer and Statistical Service Center, Indian Statistical Institute, 203, B. T. Road, 35, Calcutta, India</affiliation><affiliation>E-mail: amitava@isical.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">S.</namePart><namePart type="given">K.</namePart><namePart type="family">Parui</namePart><affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B. T. Road, 35, Calcutta, India</affiliation><affiliation>E-mail: swapan@isical.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">B.</namePart><namePart type="given">B.</namePart><namePart type="family">Chaudhuri</namePart><affiliation>Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B. T. Road, 35, Calcutta, India</affiliation><affiliation>E-mail: bbc@isical.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2002</dateIssued><copyrightDate encoding="w3cdtf">2002</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: This paper proposes an automatic recognition scheme for handprinted Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.</abstract><relatedItem type="host"><titleInfo><title>Advances in Soft Computing — AFSS 2002</title><subTitle>2002 AFSS International Conference on Fuzzy Systems Calcutta, India, February 3–6, 2002 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Nikhil</namePart><namePart type="given">R.</namePart><namePart type="family">Pal</namePart><affiliation>Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. 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