A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words
Identifieur interne : 000631 ( Istex/Corpus ); précédent : 000630; suivant : 000632A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words
Auteurs : Farshideh Einsele ; Rolf Ingold ; Jean HennebertSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character.
Url:
DOI: 10.1007/978-3-540-77046-6_63
Links to Exploration step
ISTEX:8736746BFF748D608EA398BED3ED9DAA21A88C91Le document en format XML
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We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. 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Robotics)</BookSubject><BookSubject Code="I2203X" Priority="3" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18030" Priority="5" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I13022" Priority="6" Type="Secondary">Computer Communication Networks</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Ashish</GivenName><FamilyName>Ghosh</FamilyName></EditorName><Contact><Email>ash@isical.ac.in</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Rajat</GivenName><GivenName>K.</GivenName><FamilyName>De</FamilyName></EditorName><Contact><Email>rajat@isical.ac.in</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Sankar</GivenName><GivenName>K.</GivenName><FamilyName>Pal</FamilyName></EditorName><Contact><Email>sankar@isical.ac.in</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part7"><PartInfo TocLevels="0"><PartID>7</PartID><PartSequenceNumber>7</PartSequenceNumber><PartTitle>Document Analysis and Text Mining</PartTitle><PartChapterCount>10</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Pattern Recognition and Machine Intelligence</BookTitle></PartContext></PartInfo><Chapter ID="Chap63" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>63</ChapterID><ChapterDOI>10.1007/978-3-540-77046-6_63</ChapterDOI><ChapterSequenceNumber>63</ChapterSequenceNumber><ChapterTitle Language="En">A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words</ChapterTitle><ChapterFirstPage>511</ChapterFirstPage><ChapterLastPage>518</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>7</PartID><BookID>978-3-540-77046-6</BookID><BookTitle>Pattern Recognition and Machine Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Farshideh</GivenName><FamilyName>Einsele</FamilyName></AuthorName><Contact><Email>farshideh.einsele@unifr.ch</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Rolf</GivenName><FamilyName>Ingold</FamilyName></AuthorName><Contact><Email>rolf.ingold@unifr.ch</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Jean</GivenName><FamilyName>Hennebert</FamilyName></AuthorName><Contact><Email>jean.hennebert@unifr.ch</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Université de Fribourg, Boulevard de Pérolles 90, 1700 Fribourg</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A HMM-Based Approach to Recognize Ultra Low Resolution Anti-Aliased Words</title></titleInfo><name type="personal"><namePart type="given">Farshideh</namePart><namePart type="family">Einsele</namePart><affiliation>Université de Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland</affiliation><affiliation>E-mail: farshideh.einsele@unifr.ch</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Rolf</namePart><namePart type="family">Ingold</namePart><affiliation>Université de Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland</affiliation><affiliation>E-mail: rolf.ingold@unifr.ch</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jean</namePart><namePart type="family">Hennebert</namePart><affiliation>Université de Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland</affiliation><affiliation>E-mail: jean.hennebert@unifr.ch</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: In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character.</abstract><relatedItem type="host"><titleInfo><title>Pattern Recognition and Machine Intelligence</title><subTitle>Second International Conference, PReMI 2007, Kolkata, India, December 18-22, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Ashish</namePart><namePart type="family">Ghosh</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Rajat</namePart><namePart type="given">K.</namePart><namePart type="family">De</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sankar</namePart><namePart type="given">K.</namePart><namePart type="family">Pal</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="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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