A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
Identifieur interne : 000529 ( Istex/Corpus ); précédent : 000528; suivant : 000530A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
Auteurs : Libo Fu ; Weiqiang Wang ; Yaowen ZhanSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.
Url:
DOI: 10.1007/11581772_52
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The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Libo Fu</name><affiliations><json:string>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</json:string><json:string>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</json:string><json:string>E-mail: lbfu@jdl.ac.cn</json:string></affiliations></json:item><json:item><name>Weiqiang Wang</name><affiliations><json:string>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</json:string><json:string>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</json:string><json:string>E-mail: wqwang@jdl.ac.cn</json:string></affiliations></json:item><json:item><name>Yaowen Zhan</name><affiliations><json:string>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</json:string><json:string>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</json:string><json:string>E-mail: ywzhan@jdl.ac.cn</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: In this paper we propose a robust text segmentation method in complex background. 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University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32130-9</BookID><BookTitle>Advances in Multimedia Information Processing - PCM 2005</BookTitle><BookSubTitle>6th Pacific Rim Conference on Multimedia, Jeju Island, Korea, November 13-16, 2005, Proceedings, Part I</BookSubTitle><BookVolumeNumber>3767</BookVolumeNumber><BookSequenceNumber>3767</BookSequenceNumber><BookDOI>10.1007/11581772</BookDOI><BookTitleID>127576</BookTitleID><BookPrintISBN>978-3-540-30027-4</BookPrintISBN><BookElectronicISBN>978-3-540-32130-9</BookElectronicISBN><BookChapterCount>88</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18059" Priority="1" Type="Secondary">Multimedia Information Systems</BookSubject><BookSubject Code="I18032" Priority="2" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I13022" Priority="3" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I22013" Priority="5" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I22021" Priority="6" Type="Secondary">Image Processing and Computer Vision</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Yo-Sung</GivenName><FamilyName>Ho</FamilyName></EditorName><Contact><Email>hoyo@gist.ac.kr</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Hyoung</GivenName><GivenName>Joong</GivenName><FamilyName>Kim</FamilyName></EditorName><Contact><Email>khj@korea.ac.kr</Email></Contact></Editor><Affiliation ID="Aff16"><OrgName>Gwangju Institute of Science and Technology (GIST)</OrgName><OrgAddress><Street>1 Oryong-dong Buk-gu</Street><Postcode>500-712</Postcode><City>Gwangju</City><Country>Korea</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Multimedia Security Lab</OrgDivision><OrgName>Korea University</OrgName><OrgAddress><Street>Science Campus</Street><Postcode>136-701</Postcode><City>Seoul</City><Country>Korea</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap52" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>52</ChapterID><ChapterDOI>10.1007/11581772_52</ChapterDOI><ChapterSequenceNumber>52</ChapterSequenceNumber><ChapterTitle Language="En">A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints</ChapterTitle><ChapterFirstPage>594</ChapterFirstPage><ChapterLastPage>605</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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><BookID>978-3-540-32130-9</BookID><BookTitle>Advances in Multimedia Information Processing - PCM 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18 Aff19"><AuthorName DisplayOrder="Western"><GivenName>Libo</GivenName><FamilyName>Fu</FamilyName></AuthorName><Contact><Email>lbfu@jdl.ac.cn</Email></Contact></Author><Author AffiliationIDS="Aff18 Aff19"><AuthorName DisplayOrder="Western"><GivenName>Weiqiang</GivenName><FamilyName>Wang</FamilyName></AuthorName><Contact><Email>wqwang@jdl.ac.cn</Email></Contact></Author><Author AffiliationIDS="Aff18 Aff19"><AuthorName DisplayOrder="Western"><GivenName>Yaowen</GivenName><FamilyName>Zhan</FamilyName></AuthorName><Contact><Email>ywzhan@jdl.ac.cn</Email></Contact></Author><Affiliation ID="Aff18"><OrgDivision>Institute of Computing Technology</OrgDivision><OrgName>Chinese Academy of Sciences</OrgName><OrgAddress><City>Beijing</City><Postcode>100080</Postcode><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgName>Graduate School of Chinese Academy of Sciences</OrgName><OrgAddress><City>Beijing</City><Postcode>100039</Postcode><Country>China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints</title></titleInfo><name type="personal"><namePart type="given">Libo</namePart><namePart type="family">Fu</namePart><affiliation>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</affiliation><affiliation>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</affiliation><affiliation>E-mail: lbfu@jdl.ac.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Weiqiang</namePart><namePart type="family">Wang</namePart><affiliation>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</affiliation><affiliation>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</affiliation><affiliation>E-mail: wqwang@jdl.ac.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yaowen</namePart><namePart type="family">Zhan</namePart><affiliation>Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China</affiliation><affiliation>Graduate School of Chinese Academy of Sciences, 100039, Beijing, China</affiliation><affiliation>E-mail: ywzhan@jdl.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">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</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: In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.</abstract><relatedItem type="host"><titleInfo><title>Advances in Multimedia Information Processing - PCM 2005</title><subTitle>6th Pacific Rim Conference on Multimedia, Jeju Island, Korea, November 13-16, 2005, Proceedings, Part I</subTitle></titleInfo><name type="personal"><namePart type="given">Yo-Sung</namePart><namePart type="family">Ho</namePart><affiliation>Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong Buk-gu, 500-712, Gwangju, Korea</affiliation><affiliation>E-mail: hoyo@gist.ac.kr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Hyoung</namePart><namePart type="given">Joong</namePart><namePart type="family">Kim</namePart><affiliation>Multimedia Security Lab, Korea University, Science Campus, 136-701, Seoul, Korea</affiliation><affiliation>E-mail: khj@korea.ac.kr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18059">Multimedia Information Systems</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic 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NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm 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|texte= A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
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