Text Segmentation in Complex Background Based on Color and Scale Information of Character Strokes
Identifieur interne : 001028 ( Istex/Corpus ); précédent : 001027; suivant : 001029Text Segmentation in Complex Background Based on Color and Scale Information of Character Strokes
Auteurs : Weiqiang Wang ; Libo Fu ; Wen GaoSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: This paper presents a robust approach to segmenting text embedded in complex background. Our approach consists of four steps: smart sampling, unsupervised clustering, the Bayesian decision, post-processing. The experimental results show that it works effectively, and is more efficient in removing complex background residues than the popular K-means method.
Url:
DOI: 10.1007/978-3-540-77255-2_44
Links to Exploration step
ISTEX:53048A08520B84C3555231C58815212292D5643ALe document en format XML
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TocLevels="0"><ChapterID>44</ChapterID><ChapterDOI>10.1007/978-3-540-77255-2_44</ChapterDOI><ChapterSequenceNumber>44</ChapterSequenceNumber><ChapterTitle Language="En">Text Segmentation in Complex Background Based on Color and Scale Information of Character Strokes</ChapterTitle><ChapterFirstPage>397</ChapterFirstPage><ChapterLastPage>400</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</CopyrightYear></ChapterCopyright><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>10</PartID><BookID>978-3-540-77255-2</BookID><BookTitle>Advances in Multimedia Information Processing – PCM 2007</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1 Aff2"><AuthorName DisplayOrder="Western"><GivenName>Weiqiang</GivenName><FamilyName>Wang</FamilyName></AuthorName><Contact><Email>wqwang@ict.ac.cn</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Libo</GivenName><FamilyName>Fu</FamilyName></AuthorName><Contact><Email>lbfu@ict.ac.cn</Email></Contact></Author><Author AffiliationIDS="Aff1 Aff2"><AuthorName DisplayOrder="Western"><GivenName>Wen</GivenName><FamilyName>Gao</FamilyName></AuthorName><Contact><Email>wgao@ict.ac.cn</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Institute of Computing Technology, CAS, Beijing, 100080</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Graduate School of Chinese Academy of Sciences, CAS, Beijing, 100039</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This paper presents a robust approach to segmenting text embedded in complex background. Our approach consists of four steps: smart sampling, unsupervised clustering, the Bayesian decision, post-processing. The experimental results show that it works effectively, and is more efficient in removing complex background residues than the popular K-means method.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>text segmentation</Keyword><Keyword>character stroke</Keyword><Keyword>complex background</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Text Segmentation in Complex Background Based on Color and Scale Information of Character Strokes</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Text Segmentation in Complex Background Based on Color and Scale Information of Character Strokes</title></titleInfo><name type="personal"><namePart type="given">Weiqiang</namePart><namePart type="family">Wang</namePart><affiliation>Institute of Computing Technology, CAS, Beijing, 100080, China</affiliation><affiliation>Graduate School of Chinese Academy of Sciences, CAS, Beijing, 100039, China</affiliation><affiliation>E-mail: wqwang@ict.ac.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Libo</namePart><namePart type="family">Fu</namePart><affiliation>Institute of Computing Technology, CAS, Beijing, 100080, China</affiliation><affiliation>E-mail: lbfu@ict.ac.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wen</namePart><namePart type="family">Gao</namePart><affiliation>Institute of Computing Technology, CAS, Beijing, 100080, China</affiliation><affiliation>Graduate School of Chinese Academy of Sciences, CAS, Beijing, 100039, China</affiliation><affiliation>E-mail: wgao@ict.ac.cn</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">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</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 presents a robust approach to segmenting text embedded in complex background. Our approach consists of four steps: smart sampling, unsupervised clustering, the Bayesian decision, post-processing. The experimental results show that it works effectively, and is more efficient in removing complex background residues than the popular K-means method.</abstract><relatedItem type="host"><titleInfo><title>Advances in Multimedia Information Processing – PCM 2007</title><subTitle>8th Pacific Rim Conference on Multimedia, Hong Kong, China, December 11-14, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Horace</namePart><namePart type="given">H.-S.</namePart><namePart type="family">Ip</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="given">C.</namePart><namePart type="family">Au</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Howard</namePart><namePart type="family">Leung</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ming-Ting</namePart><namePart type="family">Sun</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Wei-Ying</namePart><namePart type="family">Ma</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Shi-Min</namePart><namePart 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