Adaptive nonlinear shape matching for unconstrained handwritten character recognition
Identifieur interne : 002233 ( Istex/Corpus ); précédent : 002232; suivant : 002234Adaptive nonlinear shape matching for unconstrained handwritten character recognition
Auteurs : Jeong-Seon Park ; Seong-Whan LeeSource :
- Pattern Recognition [ 0031-3203 ] ; 1995.
Abstract
In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.
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DOI: 10.1016/0031-3203(95)00008-N
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Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:C08A93C4557ADF5AC6F4742E13829179C809FB89</idno><date when="1995" year="1995">1995</date><idno type="doi">10.1016/0031-3203(95)00008-N</idno><idno type="url">https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002233</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title><author><name sortKey="Park, Jeong Seon" sort="Park, Jeong Seon" uniqKey="Park J" first="Jeong-Seon" last="Park">Jeong-Seon Park</name><affiliation><mods:affiliation>Department of Computer Science, Korea University, 1, 5-Ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea</mods:affiliation></affiliation></author><author><name sortKey="Lee, Seong Whan" sort="Lee, Seong Whan" uniqKey="Lee S" first="Seong-Whan" last="Lee">Seong-Whan Lee</name><affiliation><mods:affiliation>Author to whom all correspondence should be addressed.</mods:affiliation></affiliation><affiliation><mods:affiliation>Software R & D Laboratories, Hyundai Electronics Industries Co. Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Pattern Recognition</title><title level="j" type="abbrev">PR</title><idno type="ISSN">0031-3203</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1995">1995</date><biblScope unit="volume">28</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="page" from="1223">1223</biblScope><biblScope unit="page" to="1235">1235</biblScope></imprint><idno type="ISSN">0031-3203</idno></series><idno type="istex">C08A93C4557ADF5AC6F4742E13829179C809FB89</idno><idno type="DOI">10.1016/0031-3203(95)00008-N</idno><idno type="PII">0031-3203(95)00008-N</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0031-3203</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Jeong-Seon Park</name><affiliations><json:string>Department of Computer Science, Korea University, 1, 5-Ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea</json:string></affiliations></json:item><json:item><name>Seong-Whan Lee</name><affiliations><json:string>Author to whom all correspondence should be addressed.</json:string><json:string>Software R & D Laboratories, Hyundai Electronics Industries Co. Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Unconstrained handwritten character recognition</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Hangul character recognition</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Adaptive nonlinear shape matching</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Local affine transformation</value></json:item></subject><language><json:string>eng</json:string></language><abstract>In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</abstract><qualityIndicators><score>6.56</score><pdfVersion>1.2</pdfVersion><pdfPageSize>533 x 771 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>4</keywordCount><abstractCharCount>928</abstractCharCount><pdfWordCount>5167</pdfWordCount><pdfCharCount>32915</pdfCharCount><pdfPageCount>13</pdfPageCount><abstractWordCount>130</abstractWordCount></qualityIndicators><title>Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title><pii><json:string>0031-3203(95)00008-N</json:string></pii><genre><json:string>research-article</json:string></genre><serie><volume>80</volume><pages><last>1058</last><first>1029</first></pages><genre></genre><language><json:string>unknown</json:string></language><title>7</title></serie><host><volume>28</volume><pii><json:string>S0031-3203(00)X0012-X</json:string></pii><pages><last>1235</last><first>1223</first></pages><issn><json:string>0031-3203</json:string></issn><issue>8</issue><genre><json:string>Journal</json:string></genre><language><json:string>unknown</json:string></language><title>Pattern Recognition</title><publicationDate>1995</publicationDate></host><categories><wos><json:string>COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE</json:string><json:string>ENGINEERING, ELECTRICAL & ELECTRONIC</json:string></wos></categories><publicationDate>1995</publicationDate><copyrightDate>1995</copyrightDate><doi><json:string>10.1016/0031-3203(95)00008-N</json:string></doi><id>C08A93C4557ADF5AC6F4742E13829179C809FB89</id><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/fulltext/pdf</uri></json:item><json:item><original>true</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/fulltext/txt</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>ELSEVIER</p></availability><date>1995</date></publicationStmt><notesStmt><note>A preliminary version of this paper has been presented at the 4th International Workshop on Frontiers in Handwriting Recognition, Taipei, Taiwan, (December 1994).</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title><author><persName><forename type="first">Jeong-Seon</forename><surname>Park</surname></persName><affiliation>Department of Computer Science, Korea University, 1, 5-Ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea</affiliation></author><author><persName><forename type="first">Seong-Whan</forename><surname>Lee</surname></persName><affiliation>Author to whom all correspondence should be addressed.</affiliation><affiliation>Software R & D Laboratories, Hyundai Electronics Industries Co. Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</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)X0012-X</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1995"></date><biblScope unit="volume">28</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="page" from="1223">1223</biblScope><biblScope unit="page" to="1235">1235</biblScope></imprint></monogr><idno type="istex">C08A93C4557ADF5AC6F4742E13829179C809FB89</idno><idno type="DOI">10.1016/0031-3203(95)00008-N</idno><idno type="PII">0031-3203(95)00008-N</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1995</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>Unconstrained handwritten character recognition</term></item><item><term>Hangul character recognition</term></item><item><term>Adaptive nonlinear shape matching</term></item><item><term>Local affine transformation</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1995-02-02">Registration</change><change when="1995-01-10">Modified</change><change when="1995">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>9500008N</aid><ce:pii>0031-3203(95)00008-N</ce:pii><ce:doi>10.1016/0031-3203(95)00008-N</ce:doi><ce:copyright type="unknown" year="1995"></ce:copyright></item-info><head><ce:article-footnote><ce:label>☆</ce:label><ce:note-para>A preliminary version of this paper has been presented at the 4th International Workshop on Frontiers in Handwriting Recognition, Taipei, Taiwan, (December 1994).</ce:note-para></ce:article-footnote><ce:title>Adaptive nonlinear shape matching for unconstrained handwritten character recognition</ce:title><ce:author-group><ce:author><ce:given-name>Jeong-Seon</ce:given-name><ce:surname>Park</ce:surname><ce:cross-ref refid="AFF1"><ce:sup>†</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>Seong-Whan</ce:given-name><ce:surname>Lee</ce:surname><ce:cross-ref refid="COR1"><ce:sup>§</ce:sup></ce:cross-ref><ce:cross-ref refid="AFF2"><ce:sup>‡</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>∗</ce:label><ce:textfn>Department of Computer Science, Korea University, 1, 5-Ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>‡</ce:label><ce:textfn>Software R & D Laboratories, Hyundai Electronics Industries Co. Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</ce:textfn></ce:affiliation><ce:correspondence id="COR1"><ce:label>§</ce:label><ce:text>Author to whom all correspondence should be addressed.</ce:text></ce:correspondence></ce:author-group><ce:date-received day="21" month="6" year="1994"></ce:date-received><ce:date-revised day="10" month="1" year="1995"></ce:date-revised><ce:date-accepted day="2" month="2" year="1995"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Unconstrained handwritten character recognition</ce:text></ce:keyword><ce:keyword><ce:text>Hangul character recognition</ce:text></ce:keyword><ce:keyword><ce:text>Adaptive nonlinear shape matching</ce:text></ce:keyword><ce:keyword><ce:text>Local affine transformation</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Adaptive nonlinear shape matching for unconstrained handwritten character recognition</title></titleInfo><name type="personal"><namePart type="given">Jeong-Seon</namePart><namePart type="family">Park</namePart><affiliation>Department of Computer Science, Korea University, 1, 5-Ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Seong-Whan</namePart><namePart type="family">Lee</namePart><affiliation>Author to whom all correspondence should be addressed.</affiliation><affiliation>Software R & D Laboratories, Hyundai Electronics Industries Co. Ltd., San 136-1 Amiri, Bubal, Ichon, Kyoungki 467-860, Korea</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">1995</dateIssued><dateValid encoding="w3cdtf">1995-02-02</dateValid><dateModified encoding="w3cdtf">1995-01-10</dateModified><copyrightDate encoding="w3cdtf">1995</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">In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.</abstract><note>A preliminary version of this paper has been presented at the 4th International Workshop on Frontiers in Handwriting Recognition, Taipei, Taiwan, (December 1994).</note><subject><genre>Keywords</genre><topic>Unconstrained handwritten character recognition</topic><topic>Hangul character recognition</topic><topic>Adaptive nonlinear shape matching</topic><topic>Local affine transformation</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">199508</dateIssued></originInfo><identifier type="ISSN">0031-3203</identifier><identifier type="PII">S0031-3203(00)X0012-X</identifier><part><date>199508</date><detail type="volume"><number>28</number><caption>vol.</caption></detail><detail type="issue"><number>8</number><caption>no.</caption></detail><extent unit="issue pages"><start>1103</start><end>1282</end></extent><extent unit="pages"><start>1223</start><end>1235</end></extent></part></relatedItem><identifier type="istex">C08A93C4557ADF5AC6F4742E13829179C809FB89</identifier><identifier type="DOI">10.1016/0031-3203(95)00008-N</identifier><identifier type="PII">0031-3203(95)00008-N</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/C08A93C4557ADF5AC6F4742E13829179C809FB89/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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