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Binary Pattern Recognition Using Markov Random Fields and HMMs

George Saon 1 Abdel Belaïd 1 
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LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We present a stochastic framework for the recognition of binary random patterns which advantageously combine HMMs and Markov random fields (MRFs). The HMM component of the model analyzes the image along one direction, in a specific state observation probability given by the product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. Experiments were performed on handwritten digits and words in a small lexicon. For the latter, we report a 89.68% average word recognition rate on the SRTP French postal cheque database (7057 words, 1779 scriptors)
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https://hal.inria.fr/inria-00537357
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Submitted on : Thursday, November 18, 2010 - 12:30:03 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM

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George Saon, Abdel Belaïd. Binary Pattern Recognition Using Markov Random Fields and HMMs. IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 1997, Apr 1997, Munich, Germany. pp.3725 - 3728, ⟨10.1109/ICASSP.1997.604678⟩. ⟨inria-00537357⟩

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