Skew Detection Algorithm for Form Document Based on Elongate Feature
Identifieur interne : 001343 ( Istex/Corpus ); précédent : 001342; suivant : 001344Skew Detection Algorithm for Form Document Based on Elongate Feature
Auteurs : Feng-Ying Xie ; Zhi-Guo Jiang ; Lei WangSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: One new and efficient skew detection algorithm is proposed for form documents according to the feature that the horizontal line has the same skew with the form. This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.
Url:
DOI: 10.1007/978-3-540-74198-5_10
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This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Feng-ying Xie</name><affiliations><json:string>Image Processing Center, BeiHang University, Beijing 100083, China</json:string><json:string>E-mail: xfy_73@buaa.edu.cn</json:string></affiliations></json:item><json:item><name>Zhi-guo Jiang</name><affiliations><json:string>Image Processing Center, BeiHang University, Beijing 100083, China</json:string></affiliations></json:item><json:item><name>Lei Wang</name><affiliations><json:string>University Duisburg-Essen Duisburg International Studies in Engineering, 47057, Germany</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: One new and efficient skew detection algorithm is proposed for form documents according to the feature that the horizontal line has the same skew with the form. 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This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I2203X</label><label>I22021</label><label>I21017</label><label>I22013</label><label>I16021</label><label>I18030</label><item><term>Computer Science</term></item><item><term>Pattern Recognition</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Artificial Intelligence (incl. 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Robotics)</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I18030" Priority="6" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Alan</GivenName><GivenName>L.</GivenName><FamilyName>Yuille</FamilyName></EditorName><Contact><Email>yuille@stat.ucla.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Song-Chun</GivenName><FamilyName>Zhu</FamilyName></EditorName><Contact><Email>sczhu@stat.ucla.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Daniel</GivenName><FamilyName>Cremers</FamilyName></EditorName><Contact><Email>dcremers@cs.uni-bonn.de</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Yongtian</GivenName><FamilyName>Wang</FamilyName></EditorName><Contact><Email>wyt@bit.edu.cn</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part2"><PartInfo TocLevels="0"><PartID>2</PartID><PartSequenceNumber>2</PartSequenceNumber><PartTitle>Applications to Faces and Text</PartTitle><PartChapterCount>6</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></PartContext></PartInfo><Chapter ID="Chap10" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>10</ChapterID><ChapterDOI>10.1007/978-3-540-74198-5_10</ChapterDOI><ChapterSequenceNumber>10</ChapterSequenceNumber><ChapterTitle Language="En">Skew Detection Algorithm for Form Document Based on Elongate Feature</ChapterTitle><ChapterFirstPage>127</ChapterFirstPage><ChapterLastPage>136</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>2</PartID><BookID>978-3-540-74198-5</BookID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Feng-ying</GivenName><FamilyName>Xie</FamilyName></AuthorName><Contact><Email>xfy_73@buaa.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Zhi-guo</GivenName><FamilyName>Jiang</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Lei</GivenName><FamilyName>Wang</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>Image Processing Center, BeiHang University, Beijing 100083</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>University Duisburg-Essen Duisburg International Studies in Engineering, 47057</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>One new and efficient skew detection algorithm is proposed for form documents according to the feature that the horizontal line has the same skew with the form. This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>form document</Keyword><Keyword>skew detection</Keyword><Keyword>directional region growing</Keyword><Keyword>connected region</Keyword><Keyword>elongate function</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Skew Detection Algorithm for Form Document Based on Elongate Feature</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Skew Detection Algorithm for Form Document Based on Elongate Feature</title></titleInfo><name type="personal"><namePart type="given">Feng-ying</namePart><namePart type="family">Xie</namePart><affiliation>Image Processing Center, BeiHang University, Beijing 100083, China</affiliation><affiliation>E-mail: xfy_73@buaa.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Zhi-guo</namePart><namePart type="family">Jiang</namePart><affiliation>Image Processing Center, BeiHang University, Beijing 100083, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Lei</namePart><namePart type="family">Wang</namePart><affiliation>University Duisburg-Essen Duisburg International Studies in Engineering, 47057, Germany</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: One new and efficient skew detection algorithm is proposed for form documents according to the feature that the horizontal line has the same skew with the form. This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.</abstract><relatedItem type="host"><titleInfo><title>Energy Minimization Methods in Computer Vision and Pattern Recognition</title><subTitle>6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Alan</namePart><namePart type="given">L.</namePart><namePart type="family">Yuille</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Song-Chun</namePart><namePart type="family">Zhu</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Daniel</namePart><namePart type="family">Cremers</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Yongtian</namePart><namePart type="family">Wang</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2007</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="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic></subject><identifier type="DOI">10.1007/978-3-540-74198-5</identifier><identifier type="ISBN">978-3-540-74195-4</identifier><identifier type="eISBN">978-3-540-74198-5</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">151892</identifier><identifier type="BookID">978-3-540-74198-5</identifier><identifier type="BookChapterCount">37</identifier><identifier type="BookVolumeNumber">4679</identifier><identifier type="BookSequenceNumber">4679</identifier><identifier type="PartChapterCount">6</identifier><part><date>2007</date><detail type="part"><title>Applications to Faces and 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type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><role><roleTerm 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