An Automatic Method for Video Character Segmentation
Identifieur interne : 000362 ( Istex/Corpus ); précédent : 000361; suivant : 000363An Automatic Method for Video Character Segmentation
Auteurs : Zohra Saidane ; Christophe GarciaSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: This paper presents an automatic segmentation system for characters in text color images cropped from natural images or videos based on a new neuronal architecture insuring fast processing and robustness against noise, variations in illumination, complex background and low resolution. An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.
Url:
DOI: 10.1007/978-3-540-69812-8_55
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An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Zohra Saidane</name><affiliations><json:string>Orange Labs, , 4 rue Clos Courtel, 35510, Cesson Sévigné, France</json:string><json:string>E-mail: zohra.saidane@orange-ftgroup.com</json:string></affiliations></json:item><json:item><name>Christophe Garcia</name><affiliations><json:string>Orange Labs, , 4 rue Clos Courtel, 35510, Cesson Sévigné, France</json:string><json:string>E-mail: christophe.garcia@orange-ftgroup.com</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: This paper presents an automatic segmentation system for characters in text color images cropped from natural images or videos based on a new neuronal architecture insuring fast processing and robustness against noise, variations in illumination, complex background and low resolution. An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.</abstract><qualityIndicators><score>6.534</score><pdfVersion>1.6</pdfVersion><pdfPageSize>430 x 660 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>1025</abstractCharCount><pdfWordCount>3090</pdfWordCount><pdfCharCount>17645</pdfCharCount><pdfPageCount>10</pdfPageCount><abstractWordCount>162</abstractWordCount></qualityIndicators><title>An Automatic Method for Video Character Segmentation</title><genre.original><json:string>OriginalPaper</json:string></genre.original><chapterId><json:string>55</json:string><json:string>Chap55</json:string></chapterId><genre><json:string>conference [eBooks]</json:string></genre><serie><editor><json:item><name>David Hutchison</name></json:item><json:item><name>Takeo Kanade</name></json:item><json:item><name>Josef Kittler</name></json:item><json:item><name>Jon M. 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An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Video OCR</Keyword><Keyword>Text Segmentation</Keyword><Keyword>Character recognition</Keyword><Keyword>Neural Networks</Keyword><Keyword>Image Processing</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>An Automatic Method for Video Character Segmentation</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>An Automatic Method for Video Character Segmentation</title></titleInfo><name type="personal"><namePart type="given">Zohra</namePart><namePart type="family">Saidane</namePart><affiliation>Orange Labs, , 4 rue Clos Courtel, 35510, Cesson Sévigné, France</affiliation><affiliation>E-mail: zohra.saidane@orange-ftgroup.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Christophe</namePart><namePart type="family">Garcia</namePart><affiliation>Orange Labs, , 4 rue Clos Courtel, 35510, Cesson Sévigné, France</affiliation><affiliation>E-mail: christophe.garcia@orange-ftgroup.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">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</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 an automatic segmentation system for characters in text color images cropped from natural images or videos based on a new neuronal architecture insuring fast processing and robustness against noise, variations in illumination, complex background and low resolution. An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.</abstract><relatedItem type="host"><titleInfo><title>Image Analysis and Recognition</title><subTitle>5th International Conference, ICIAR 2008, Póvoa de Varzim, Portugal, June 25-27, 2008. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Aurélio</namePart><namePart type="family">Campilho</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Mohamed</namePart><namePart type="family">Kamel</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2008</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="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22040">Biometrics</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></subject><identifier type="DOI">10.1007/978-3-540-69812-8</identifier><identifier type="ISBN">978-3-540-69811-1</identifier><identifier type="eISBN">978-3-540-69812-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">162466</identifier><identifier type="BookID">978-3-540-69812-8</identifier><identifier type="BookChapterCount">111</identifier><identifier type="BookVolumeNumber">5112</identifier><identifier type="BookSequenceNumber">5112</identifier><identifier type="PartChapterCount">5</identifier><part><date>2008</date><detail type="part"><title>Character Recognition</title></detail><detail type="volume"><number>5112</number><caption>vol.</caption></detail><extent 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type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><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><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart 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 type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>serial</issuance></originInfo><identifier 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