inria-00539591, version 1
High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Field
International Journal of Pattern Recognition and Artificial Intelligence 11, 5 (1997) 771-788
Résumé : In this paper we present a system for the recognition of handwritten words on literal check amounts which advantageously combine HMMs and Markov random fields (MRFs). It operates at pixel level, in a holistic manner, on height normalized word images which are viewed as random field realizations. The HMM analyzes the image along the horizontal writing direction, in a specific state observation probability given by the column product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. We report a 90.08% average word recognition rate on 2378 words and a 79.52% amount rate on 579 amounts of the SRTP (Service de Recherche Technique de la Poste) French postal check database (7031 words, 1779 amounts, different scriptors).
- 1 :
- INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
- Domaine : Informatique/Bibliothèque électronique
- Mots-clés : Pattern recognition – Hand writing – Word – Markov model – Random field – Hidden Markov model || Reconnaissance forme – Ecriture – Mot – Modèle Markov – Champ aléatoire – Chèque postal – Modèle Markov variable cachée
- inria-00539591, version 1
- http://hal.inria.fr/inria-00539591
- oai:hal.inria.fr:inria-00539591
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- Soumis le : Mercredi 24 Novembre 2010, 17:07:51
- Dernière modification le : Mercredi 24 Novembre 2010, 17:07:51
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