Improvement of handwritten Japanese character recognition using weighted direction code histogram
Identifieur interne : 002643 ( Istex/Corpus ); précédent : 002642; suivant : 002644Improvement of handwritten Japanese character recognition using weighted direction code histogram
Auteurs : Fumitaka Kimura ; Tetsushi Wakabayashi ; Shinji Tsuruoka ; Yasuji MiyakeSource :
- Pattern Recognition [ 0031-3203 ] ; 1996.
Abstract
Several algorithms for preprocessing, feature extraction, pre-classification, and main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition. The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. This accuracy is the best one ever reported for the database.
Url:
DOI: 10.1016/S0031-3203(96)00153-7
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The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. This accuracy is the best one ever reported for the database.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Fumitaka Kimura</name><affiliations><json:string>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</json:string><json:string>E-mail: kimura@info.mieu.ac.jp</json:string></affiliations></json:item><json:item><name>Tetsushi Wakabayashi</name><affiliations><json:string>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</json:string></affiliations></json:item><json:item><name>Shinji Tsuruoka</name><affiliations><json:string>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</json:string></affiliations></json:item><json:item><name>Yasuji Miyake</name><affiliations><json:string>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Character recognition</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Nonlinear normalization</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Feature extraction</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Discriminant analysis</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Statistical pattern recognition</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Classification</value></json:item></subject><language><json:string>eng</json:string></language><abstract>Several algorithms for preprocessing, feature extraction, pre-classification, and main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition. The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. 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Tel.: (592) 31–9457; fax: (592) 31–9456</affiliation><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation></author><author><persName><forename type="first">Tetsushi</forename><surname>Wakabayashi</surname></persName><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation></author><author><persName><forename type="first">Shinji</forename><surname>Tsuruoka</surname></persName><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation></author><author><persName><forename type="first">Yasuji</forename><surname>Miyake</surname></persName><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation></author></analytic><monogr><title level="j">Pattern Recognition</title><title level="j" type="abbrev">PR</title><idno type="pISSN">0031-3203</idno><idno type="PII">S0031-3203(00)X0030-1</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1996"></date><biblScope unit="volume">30</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="page" from="1329">1329</biblScope><biblScope unit="page" to="1337">1337</biblScope></imprint></monogr><idno type="istex">36AB2450DF5EF0BC39A25ED934B795EB1BCC9088</idno><idno type="DOI">10.1016/S0031-3203(96)00153-7</idno><idno type="PII">S0031-3203(96)00153-7</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1997</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Several algorithms for preprocessing, feature extraction, pre-classification, and main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition. The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. This accuracy is the best one ever reported for the database.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>Character recognition</term></item><item><term>Nonlinear normalization</term></item><item><term>Feature extraction</term></item><item><term>Discriminant analysis</term></item><item><term>Statistical pattern recognition</term></item><item><term>Classification</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1996-07-16">Received</change><change when="1996-10-15">Modified</change><change when="1996">Published</change></revisionDesc></teiHeader></istex:fulltextTEI></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>PR</jid><aid>96001537</aid><ce:pii>S0031-3203(96)00153-7</ce:pii><ce:doi>10.1016/S0031-3203(96)00153-7</ce:doi><ce:copyright type="unknown" year="1997"></ce:copyright></item-info><head><ce:title>Improvement of handwritten Japanese character recognition using weighted direction code histogram</ce:title><ce:author-group><ce:author><ce:given-name>Fumitaka</ce:given-name><ce:surname>Kimura</ce:surname><ce:cross-ref refid="COR1"><ce:sup>∗</ce:sup></ce:cross-ref><ce:e-address>kimura@info.mieu.ac.jp</ce:e-address></ce:author><ce:author><ce:given-name>Tetsushi</ce:given-name><ce:surname>Wakabayashi</ce:surname></ce:author><ce:author><ce:given-name>Shinji</ce:given-name><ce:surname>Tsuruoka</ce:surname></ce:author><ce:author><ce:given-name>Yasuji</ce:given-name><ce:surname>Miyake</ce:surname></ce:author><ce:affiliation><ce:textfn>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</ce:textfn></ce:affiliation><ce:correspondence id="COR1"><ce:label>∗</ce:label><ce:text>Author to whom correspondence should be addressed. Tel.: (592) 31–9457; fax: (592) 31–9456</ce:text></ce:correspondence></ce:author-group><ce:date-received day="16" month="7" year="1996"></ce:date-received><ce:date-revised day="15" month="10" year="1996"></ce:date-revised><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Several algorithms for preprocessing, feature extraction, pre-classification, and main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition. The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. This accuracy is the best one ever reported for the database.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Character recognition</ce:text></ce:keyword><ce:keyword><ce:text>Nonlinear normalization</ce:text></ce:keyword><ce:keyword><ce:text>Feature extraction</ce:text></ce:keyword><ce:keyword><ce:text>Discriminant analysis</ce:text></ce:keyword><ce:keyword><ce:text>Statistical pattern recognition</ce:text></ce:keyword><ce:keyword><ce:text>Classification</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Improvement of handwritten Japanese character recognition using weighted direction code histogram</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Improvement of handwritten Japanese character recognition using weighted direction code histogram</title></titleInfo><name type="personal"><namePart type="given">Fumitaka</namePart><namePart type="family">Kimura</namePart><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation><affiliation>E-mail: kimura@info.mieu.ac.jp</affiliation><description>Author to whom correspondence should be addressed. Tel.: (592) 31–9457; fax: (592) 31–9456</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Tetsushi</namePart><namePart type="family">Wakabayashi</namePart><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Shinji</namePart><namePart type="family">Tsuruoka</namePart><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yasuji</namePart><namePart type="family">Miyake</namePart><affiliation>Faculty of Engineering, Mie University, 1515 Kamihama, Tsu, Mie 514, Japan</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1996</dateIssued><dateCaptured encoding="w3cdtf">1996-07-16</dateCaptured><dateModified encoding="w3cdtf">1996-10-15</dateModified><copyrightDate encoding="w3cdtf">1997</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Several algorithms for preprocessing, feature extraction, pre-classification, and main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition. The compared algorithms are three types of nonlinear normalization for the preprocessing, the discriminant analysis and the principal component analysis for the feature extraction, the minimum distance classifiers and the linear classifier for the high-speed pre-classification, and modified Bayes classifier and subspace method for the robust main classification. The performance of the recognition algorithm is fully tested using the ETL9B character database. The recognition accuracy of 99.15% at the recognition speed of eight characters per second is achieved. This accuracy is the best one ever reported for the database.</abstract><subject><genre>Keywords</genre><topic>Character recognition</topic><topic>Nonlinear normalization</topic><topic>Feature extraction</topic><topic>Discriminant analysis</topic><topic>Statistical pattern recognition</topic><topic>Classification</topic></subject><relatedItem type="host"><titleInfo><title>Pattern Recognition</title></titleInfo><titleInfo type="abbreviated"><title>PR</title></titleInfo><genre type="Journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199708</dateIssued></originInfo><identifier type="ISSN">0031-3203</identifier><identifier type="PII">S0031-3203(00)X0030-1</identifier><part><date>199708</date><detail type="issue"><title>Oriental Character Recognition</title></detail><detail type="volume"><number>30</number><caption>vol.</caption></detail><detail type="issue"><number>8</number><caption>no.</caption></detail><extent unit="issue pages"><start>1253</start><end>1371</end></extent><extent unit="pages"><start>1329</start><end>1337</end></extent></part></relatedItem><identifier type="istex">36AB2450DF5EF0BC39A25ED934B795EB1BCC9088</identifier><identifier type="DOI">10.1016/S0031-3203(96)00153-7</identifier><identifier type="PII">S0031-3203(96)00153-7</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/36AB2450DF5EF0BC39A25ED934B795EB1BCC9088/enrichments/catWos"><teiHeader><profileDesc><textClass><classCode scheme="WOS">COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE</classCode><classCode scheme="WOS">ENGINEERING, ELECTRICAL & ELECTRONIC</classCode></textClass></profileDesc></teiHeader></istex:catWosTEI></enrichments></istex></record>Pour manipuler ce document sous Unix (Dilib)
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