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Journal Articles International Journal of Pattern Recognition and Artificial Intelligence Year : 1997

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

George Saon
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  • PersonId : 757082
  • IdRef : 16037636X
Abdel Belaïd
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  • PersonId : 830137

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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Dates and versions

inria-00539591 , version 1 (24-11-2010)

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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, 1997, 11 (5), pp.771-788. ⟨10.1142/S0218001497000342⟩. ⟨inria-00539591⟩
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