Hidden Markov models in text recognition
Identifieur interne : 00C603 ( Main/Exploration ); précédent : 00C602; suivant : 00C604Hidden Markov models in text recognition
Auteurs : J.-C. Anigbogu [États-Unis] ; Abdel Belaïd [France]Source :
- International Journal of Pattern Recognition and Artificial Intelligence [ 0218-0014 ] ; 1995.
Descripteurs français
- mix :
- Arbre décision, Character recognition, Correction erreur, Decision tree, Détection erreur, Error correction, Error detection, Euclidean distance, Hidden Markov model, Information system, Majority vote, Markov model, Modèle Markov, Multifont character recognition, Ordre 2, Performance système, Reconnaissance caractère, Second order, System performance, Système information, Viterbi algorithm.
Abstract
A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for advice before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.
Url:
DOI: 10.1142/S0218001495000389
Affiliations:
- France, États-Unis
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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Le document en format XML
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The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for advice before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.</div></front></TEI><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="États-Unis"><noRegion><name sortKey="Anigbogu, J C" sort="Anigbogu, J C" uniqKey="Anigbogu J" first="J.-C." last="Anigbogu">J.-C. Anigbogu</name></noRegion></country><country name="France"><region name="Grand Est"><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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