High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Field

George Saon 1 Abdel Belaïd 1
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LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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).
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International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 1997, 11 (5), pp.771-788. 〈10.1142/S0218001497000342〉
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Contributeur : Abdel Belaid <>
Soumis le : mercredi 24 novembre 2010 - 17:07:51
Dernière modification le : mardi 24 avril 2018 - 13:51:28

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George Saon, Abdel Belaïd. High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Field. International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 1997, 11 (5), pp.771-788. 〈10.1142/S0218001497000342〉. 〈inria-00539591〉

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