ApOFIS: an A priori optical font identification system
Identifieur interne : 001062 ( Istex/Corpus ); précédent : 001061; suivant : 001063ApOFIS: an A priori optical font identification system
Auteurs : Abdelwahab Zramdini ; Rolf IngoldSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1995.
Abstract
Abstract: The detection of the font style, point size, etc. of a text is an obvious way to improve the capabilities of text recognition algorithms. The ApOFIS system has been designed in order to satisfy such a requirement. It adopts an a priori font identification approach where the recognition of a text font is done without considering the characters that appear in the text. In ApOFIS, a font is characterized especially by its family, weight, slope and size. Features used in the system represent global aspects of text line images. They have been extracted essentially from projection profiles and from connected components bounding boxes. Statistical tests have revealed that these features follow approximately normal laws so that parameter estimation is used in learning. A multivariate Bayesian classifier, based on these features, has been designed for font recognition and applied on a base of 240 font models created from a training set of texts written with these fonts. On text lines having the same length as those used for learning, the system allows to discriminate fonts with an average accuracy of 96.5% for top choice and 98.3% within the two top choices.
Url:
DOI: 10.1007/3-540-60298-4_309
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ISTEX:22BF2D5D7C2EBF06E861DEA7D39DF32CD00BAD65Le document en format XML
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The ApOFIS system has been designed in order to satisfy such a requirement. It adopts an a priori font identification approach where the recognition of a text font is done without considering the characters that appear in the text. In ApOFIS, a font is characterized especially by its family, weight, slope and size. Features used in the system represent global aspects of text line images. They have been extracted essentially from projection profiles and from connected components bounding boxes. Statistical tests have revealed that these features follow approximately normal laws so that parameter estimation is used in learning. A multivariate Bayesian classifier, based on these features, has been designed for font recognition and applied on a base of 240 font models created from a training set of texts written with these fonts. On text lines having the same length as those used for learning, the system allows to discriminate fonts with an average accuracy of 96.5% for top choice and 98.3% within the two top choices.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Abdelwahab Zramdini</name><affiliations><json:string>Institute of Informatics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland</json:string><json:string>E-mail: abdelwahab.zramdini@unifr.ch</json:string></affiliations></json:item><json:item><name>Rolf Ingold</name><affiliations><json:string>Institute of Informatics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: The detection of the font style, point size, etc. of a text is an obvious way to improve the capabilities of text recognition algorithms. 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The ApOFIS system has been designed in order to satisfy such a requirement. It adopts an a priori font identification approach where the recognition of a text font is done without considering the characters that appear in the text. In ApOFIS, a font is characterized especially by its family, weight, slope and size. Features used in the system represent global aspects of text line images. They have been extracted essentially from projection profiles and from connected components bounding boxes. Statistical tests have revealed that these features follow approximately normal laws so that parameter estimation is used in learning. A multivariate Bayesian classifier, based on these features, has been designed for font recognition and applied on a base of 240 font models created from a training set of texts written with these fonts. On text lines having the same length as those used for learning, the system allows to discriminate fonts with an average accuracy of 96.5% for top choice and 98.3% within the two top choices.</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>I22021</label><label>I2203X</label><label>I22013</label><label>I21017</label><label>I14002</label><label>I23044</label><item><term>Computer Science</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Pattern Recognition</term></item><item><term>Computer Graphics</term></item><item><term>Artificial Intelligence (incl. 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The <Emphasis Type="Italic">ApOFIS</Emphasis> system has been designed in order to satisfy such a requirement. It adopts an a priori font identification approach where the recognition of a text font is done without considering the characters that appear in the text. In <Emphasis Type="Italic">ApOFIS</Emphasis>, a font is characterized especially by its family, weight, slope and size. Features used in the system represent global aspects of text line images. They have been extracted essentially from projection profiles and from connected components bounding boxes. Statistical tests have revealed that these features follow approximately normal laws so that parameter estimation is used in learning.</Para><Para>A multivariate Bayesian classifier, based on these features, has been designed for font recognition and applied on a base of 240 font models created from a training set of texts written with these fonts. On text lines having the same length as those used for learning, the system allows to discriminate fonts with an average accuracy of 96.5% for top choice and 98.3% within the two top choices.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Document Analysis</Keyword><Keyword>a priori Font Recognition</Keyword><Keyword>Bayesian Classifier</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>ApOFIS: an A priori optical font identification system</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>ApOFIS: an A priori optical font identification system</title></titleInfo><name type="personal"><namePart type="given">Abdelwahab</namePart><namePart type="family">Zramdini</namePart><affiliation>Institute of Informatics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland</affiliation><affiliation>E-mail: abdelwahab.zramdini@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>Institute of Informatics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="ReviewPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">1995</dateIssued><copyrightDate encoding="w3cdtf">1995</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: The detection of the font style, point size, etc. of a text is an obvious way to improve the capabilities of text recognition algorithms. The ApOFIS system has been designed in order to satisfy such a requirement. It adopts an a priori font identification approach where the recognition of a text font is done without considering the characters that appear in the text. In ApOFIS, a font is characterized especially by its family, weight, slope and size. Features used in the system represent global aspects of text line images. They have been extracted essentially from projection profiles and from connected components bounding boxes. Statistical tests have revealed that these features follow approximately normal laws so that parameter estimation is used in learning. A multivariate Bayesian classifier, based on these features, has been designed for font recognition and applied on a base of 240 font models created from a training set of texts written with these fonts. On text lines having the same length as those used for learning, the system allows to discriminate fonts with an average accuracy of 96.5% for top choice and 98.3% within the two top choices.</abstract><relatedItem type="host"><titleInfo><title>Image Analysis and Processing</title><subTitle>8th International Conference, ICIAP'95 San Remo, Italy, September 13–15, 1995 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Carlo</namePart><namePart type="family">Braccini</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Leila</namePart><namePart type="family">DeFloriani</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gianni</namePart><namePart type="family">Vernazza</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">1995</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="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I14002">Software Engineering/Programming and Operating Systems</topic><topic authority="SpringerSubjectCodes" authorityURI="I23044">Computer-Aided Engineering (CAD, CAE) and Design</topic></subject><identifier type="DOI">10.1007/3-540-60298-4</identifier><identifier type="ISBN">978-3-540-60298-9</identifier><identifier type="eISBN">978-3-540-44787-0</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">42645</identifier><identifier type="BookID">3540602984</identifier><identifier type="BookChapterCount">114</identifier><identifier type="BookVolumeNumber">974</identifier><part><date>1995</date><detail type="volume"><number>974</number><caption>vol.</caption></detail><extent unit="pages"><start>527</start><end>532</end></extent></part><recordInfo><recordOrigin>Springer-Verlag, 1995</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Goos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Juris</namePart><namePart type="family">Hartmanis</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jan</namePart><namePart type="family">van Leeuwen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">1995</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag, 1995</recordOrigin></recordInfo></relatedItem><identifier type="istex">22BF2D5D7C2EBF06E861DEA7D39DF32CD00BAD65</identifier><identifier type="DOI">10.1007/3-540-60298-4_309</identifier><identifier type="ChapterID">81</identifier><identifier type="ChapterID">Chap81</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag, 1995</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag, 1995</recordOrigin></recordInfo></mods></metadata><enrichments><istex:refBibTEI uri="https://api.istex.fr/document/22BF2D5D7C2EBF06E861DEA7D39DF32CD00BAD65/enrichments/refBib"><teiHeader></teiHeader><text><front></front><body></body><back><listBibl><biblStruct xml:id="b0"><monogr><title></title><author><persName><forename type="first">T</forename><surname>Bayer</surname></persName></author><author><persName><forename type="first">J</forename><surname>Hull</surname></persName></author><author><persName><forename type="first">G</forename><surname>Nagy</surname></persName></author><editor>Structured Document Image Analysis, eds., H.S. 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