Stroke extraction for chinese characters using a trend-followed transcribing technique
Identifieur interne : 000E64 ( Istex/Corpus ); précédent : 000E63; suivant : 000E65Stroke extraction for chinese characters using a trend-followed transcribing technique
Auteurs : Ji-Rong Lin ; Chang-Fuu ChenSource :
- Pattern Recognition [ 0031-3203 ] ; 1996.
Abstract
The merit of the stroke extraction algorithms which utilize the thinning process is the ease of the feature abstracting from the skeleton of a character. The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. That is, the proposed stroke extraction algorithm is useful and reliable.
Url:
DOI: 10.1016/0031-3203(96)00039-8
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Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:81F9049326D53BBCD15C6D2F0312B64285E84990</idno><date when="1996" year="1996">1996</date><idno type="doi">10.1016/0031-3203(96)00039-8</idno><idno type="url">https://api.istex.fr/document/81F9049326D53BBCD15C6D2F0312B64285E84990/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000E64</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Stroke extraction for chinese characters using a trend-followed transcribing technique</title><author><name sortKey="Lin, Ji Rong" sort="Lin, Ji Rong" uniqKey="Lin J" first="Ji-Rong" last="Lin">Ji-Rong Lin</name><affiliation><mods:affiliation>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</mods:affiliation></affiliation></author><author><name sortKey="Chen, Chang Fuu" sort="Chen, Chang Fuu" uniqKey="Chen C" first="Chang-Fuu" last="Chen">Chang-Fuu Chen</name><affiliation><mods:affiliation>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</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="1996">1996</date><biblScope unit="volume">29</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="1789">1789</biblScope><biblScope unit="page" to="1805">1805</biblScope></imprint><idno type="ISSN">0031-3203</idno></series><idno type="istex">81F9049326D53BBCD15C6D2F0312B64285E84990</idno><idno type="DOI">10.1016/0031-3203(96)00039-8</idno><idno type="PII">0031-3203(96)00039-8</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">The merit of the stroke extraction algorithms which utilize the thinning process is the ease of the feature abstracting from the skeleton of a character. The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. That is, the proposed stroke extraction algorithm is useful and reliable.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Ji-rong Lin</name><affiliations><json:string>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</json:string></affiliations></json:item><json:item><name>Chang-Fuu Chen</name><affiliations><json:string>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. 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This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. 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The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. 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Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</ce:textfn></ce:affiliation></ce:author-group><ce:date-received day="1" month="2" year="1994"></ce:date-received><ce:date-revised day="15" month="12" year="1995"></ce:date-revised><ce:date-accepted day="4" month="4" year="1996"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>The merit of the stroke extraction algorithms which utilize the thinning process is the ease of the feature abstracting from the skeleton of a character. The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. That is, the proposed stroke extraction algorithm is useful and reliable.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Chinese character stroke extraction</ce:text></ce:keyword><ce:keyword><ce:text>Thinning process</ce:text></ce:keyword><ce:keyword><ce:text>Travel algorithm</ce:text></ce:keyword><ce:keyword><ce:text>Trend-followed</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Stroke extraction for chinese characters using a trend-followed transcribing technique</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Stroke extraction for chinese characters using a trend-followed transcribing technique</title></titleInfo><name type="personal"><namePart type="given">Ji-rong</namePart><namePart type="family">Lin</namePart><affiliation>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Chang-Fuu</namePart><namePart type="family">Chen</namePart><affiliation>Department of Electrical Engineering, Tatung Institute of Technology, 40, Chung-Shan N. Road, 3rd Sec., 10451, Taipei, Taiwan, R.O.C.</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><dateValid encoding="w3cdtf">1996-04-04</dateValid><dateModified encoding="w3cdtf">1995-12-15</dateModified><copyrightDate encoding="w3cdtf">1996</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">The merit of the stroke extraction algorithms which utilize the thinning process is the ease of the feature abstracting from the skeleton of a character. The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. That is, the proposed stroke extraction algorithm is useful and reliable.</abstract><subject><genre>Keywords</genre><topic>Chinese character stroke extraction</topic><topic>Thinning process</topic><topic>Travel algorithm</topic><topic>Trend-followed</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">199611</dateIssued></originInfo><identifier type="ISSN">0031-3203</identifier><identifier type="PII">S0031-3203(00)X0022-2</identifier><part><date>199611</date><detail type="volume"><number>29</number><caption>vol.</caption></detail><detail type="issue"><number>11</number><caption>no.</caption></detail><extent unit="issue pages"><start>1761</start><end>1930</end></extent><extent unit="pages"><start>1789</start><end>1805</end></extent></part></relatedItem><identifier type="istex">81F9049326D53BBCD15C6D2F0312B64285E84990</identifier><identifier type="DOI">10.1016/0031-3203(96)00039-8</identifier><identifier type="PII">0031-3203(96)00039-8</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/81F9049326D53BBCD15C6D2F0312B64285E84990/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><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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