Hidden Markov models in text recognition

J.-C. Anigbogu 1 Abdel Belaïd 2
2 READ - READ
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
Type de document :
Article dans une revue
International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 1995, 9 (6), pp.925-958. 〈10.1142/S0218001495000389〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00533980
Contributeur : Abdel Belaid <>
Soumis le : lundi 8 novembre 2010 - 15:50:33
Dernière modification le : mardi 24 avril 2018 - 13:51:28

Identifiants

Collections

Citation

J.-C. Anigbogu, Abdel Belaïd. Hidden Markov models in text recognition. International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 1995, 9 (6), pp.925-958. 〈10.1142/S0218001495000389〉. 〈inria-00533980〉

Partager

Métriques

Consultations de la notice

148