A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM
Identifieur interne : 000384 ( Istex/Corpus ); précédent : 000383; suivant : 000385A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM
Auteurs : Hamed Pirsiavash ; Ramin Mehran ; Farbod RazzaziSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.
Url:
DOI: 10.1007/11578079_63
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Proceedings</BookSubTitle><BookVolumeNumber>3773</BookVolumeNumber><BookSequenceNumber>3773</BookSequenceNumber><BookDOI>10.1007/11578079</BookDOI><BookTitleID>127477</BookTitleID><BookPrintISBN>978-3-540-29850-2</BookPrintISBN><BookElectronicISBN>978-3-540-32242-9</BookElectronicISBN><BookChapterCount>110</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22005" Priority="4" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Alberto</GivenName><FamilyName>Sanfeliu</FamilyName></EditorName><Contact><Email>asanfeliu@iri.upc.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Manuel</GivenName><GivenName>Lazo</GivenName><FamilyName>Cortés</FamilyName></EditorName><Contact><Email>mlazo@icmf.inf.cu</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Dept. System Engineering and Automation</OrgDivision><OrgName>Universitat Politècnica de Catalunya (UPC) Barcelona</OrgName><OrgAddress><Country>Spain</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Pattern Recognition Group, ICIMAF</OrgName><OrgAddress><City>Havana</City><Country>Cuba</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Regular Papers</PartTitle><PartChapterCount>102</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Progress in Pattern Recognition, Image Analysis and Applications</BookTitle></PartContext></PartInfo><Chapter ID="Chap63" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>63</ChapterID><ChapterDOI>10.1007/11578079_63</ChapterDOI><ChapterSequenceNumber>63</ChapterSequenceNumber><ChapterTitle Language="En">A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM</ChapterTitle><ChapterFirstPage>601</ChapterFirstPage><ChapterLastPage>610</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><PartID>1</PartID><BookID>978-3-540-32242-9</BookID><BookTitle>Progress in Pattern Recognition, Image Analysis and Applications</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18 Aff20"><AuthorName DisplayOrder="Western"><GivenName>Hamed</GivenName><FamilyName>Pirsiavash</FamilyName></AuthorName><Contact><Email>h_pirsiavash@mehr.sharif.edu</Email></Contact></Author><Author AffiliationIDS="Aff19 Aff20"><AuthorName DisplayOrder="Western"><GivenName>Ramin</GivenName><FamilyName>Mehran</FamilyName></AuthorName><Contact><Email>rmehran@gmail.com</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Farbod</GivenName><FamilyName>Razzazi</FamilyName></AuthorName><Contact><Email>razzazi@payasoft.com</Email></Contact></Author><Affiliation ID="Aff18"><OrgDivision>Department of Electrical Engineering</OrgDivision><OrgName>Sharif University of Technology</OrgName><OrgAddress><City>Tehran</City><Country>Iran</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Department of Electrical Engineering</OrgDivision><OrgName>K.N.Toosi Univ. of Tech.</OrgName><OrgAddress><City>Tehran</City><Country>Iran</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgName>Paya Soft co.</OrgName><OrgAddress><City>Tehran</City><Country>Iran</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM</title></titleInfo><name type="personal"><namePart type="given">Hamed</namePart><namePart type="family">Pirsiavash</namePart><affiliation>Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran</affiliation><affiliation>Paya Soft co., Tehran, Iran</affiliation><affiliation>E-mail: h_pirsiavash@mehr.sharif.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ramin</namePart><namePart type="family">Mehran</namePart><affiliation>Department of Electrical Engineering, K.N.Toosi Univ. of Tech., Tehran, Iran</affiliation><affiliation>Paya Soft co., Tehran, Iran</affiliation><affiliation>E-mail: rmehran@gmail.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Farbod</namePart><namePart type="family">Razzazi</namePart><affiliation>Paya Soft co., Tehran, Iran</affiliation><affiliation>E-mail: razzazi@payasoft.com</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: Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.</abstract><relatedItem type="host"><titleInfo><title>Progress in Pattern Recognition, Image Analysis and Applications</title><subTitle>10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Alberto</namePart><namePart type="family">Sanfeliu</namePart><affiliation>Dept. 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