Application of Hidden Markov Models to Multifont Next Recognition
Identifieur interne : 000B57 ( Crin/Corpus ); précédent : 000B56; suivant : 000B58Application of Hidden Markov Models to Multifont Next Recognition
Auteurs : J.-C. Anigbogu ; A. BelaïdSource :
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Abstract
This paper describes a multifont recognition system that uses Hidden Markov Models. This system works in two phases. In the first phase, it detects the predominant font in the current paragraph and then uses the Modified Viterbi Algorithm to recognize the characters. The choice of the predominant font and the examination of certain features permit a further reduction of the number of models to be traited. The second phase deals with the grouping of characters into words and verification using an application dictionary or digrams. Character recognition results on ten fonts range between 96========percnt; and 99.82========percnt; depending on the font.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="481">Application of Hidden Markov Models to Multifont Next Recognition</title></titleStmt><publicationStmt><idno type="RBID">CRIN:anigbogu91b</idno><date when="1991" year="1991">1991</date><idno type="wicri:Area/Crin/Corpus">000B57</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Application of Hidden Markov Models to Multifont Next Recognition</title><author><name sortKey="Anigbogu, J C" sort="Anigbogu, J C" uniqKey="Anigbogu J" first="J.-C." last="Anigbogu">J.-C. Anigbogu</name></author><author><name sortKey="Belaid, A" sort="Belaid, A" uniqKey="Belaid A" first="A." last="Belaïd">A. Belaïd</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Hidden Markov Models</term><term>Viterbi algorithm</term><term>font determination</term><term>multifont character recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="1930">This paper describes a multifont recognition system that uses Hidden Markov Models. This system works in two phases. In the first phase, it detects the predominant font in the current paragraph and then uses the Modified Viterbi Algorithm to recognize the characters. The choice of the predominant font and the examination of certain features permit a further reduction of the number of models to be traited. The second phase deals with the grouping of characters into words and verification using an application dictionary or digrams. Character recognition results on ten fonts range between 96========percnt; and 99.82========percnt; depending on the font.</div></front></TEI><BibTex type="inproceedings"><ref>anigbogu91b</ref><crinnumber>91-R-081</crinnumber><category>3</category><equipe>RFIA</equipe><author><e>Anigbogu, J.-C.</e><e>Belaïd, A.</e></author><title>Application of Hidden Markov Models to Multifont Next Recognition</title><booktitle>{Proceedings First International Conference on Document Analysis and Recognition, Saint-Malo}</booktitle><year>1991</year><volume>2</volume><pages>785-793</pages><month>oct</month><keywords><e>multifont character recognition</e><e>Hidden Markov Models</e><e>font determination</e><e>Viterbi algorithm</e></keywords><abstract>This paper describes a multifont recognition system that uses Hidden Markov Models. This system works in two phases. In the first phase, it detects the predominant font in the current paragraph and then uses the Modified Viterbi Algorithm to recognize the characters. The choice of the predominant font and the examination of certain features permit a further reduction of the number of models to be traited. The second phase deals with the grouping of characters into words and verification using an application dictionary or digrams. Character recognition results on ten fonts range between 96========percnt; and 99.82========percnt; depending on the font.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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