Analytic word recognition without segmentation based on Markov random fields

Christophe Choisy 1 Abdel Belaïd 1
1 READ - READ
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, a method for analytic handwritten word recognition based on causal Markov random fields is described. The words models are hmms where each state corresponds to a letter; each letter is modelled by a nshp-hmm (Markov field). Global models are build dynamically, and used for recognition and learning with the baum-welch algorithm. Learning of letter and word models is made using the parameters reestimated on the generated global models. No segmentation is necessary: the system determines itself the best limits between the letters during learning. First experiments on a real base of french check amount words give encouraging results of 83.4% for recognition.
Type de document :
Communication dans un congrès
7th International Workshop on Frontiers in Handwriting Recognition - IWFHR'2000, 2000, Amsterdam, Hollande, 2000
Liste complète des métadonnées

Littérature citée [12 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00099041
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 08:47:39
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59
Document(s) archivé(s) le : mercredi 29 mars 2017 - 12:36:17

Fichiers

Identifiants

  • HAL Id : inria-00099041, version 1

Collections

Citation

Christophe Choisy, Abdel Belaïd. Analytic word recognition without segmentation based on Markov random fields. 7th International Workshop on Frontiers in Handwriting Recognition - IWFHR'2000, 2000, Amsterdam, Hollande, 2000. 〈inria-00099041〉

Partager

Métriques

Consultations de la notice

104

Téléchargements de fichiers

31