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Analytic word recognition without segmentation based on Markov random fields

Christophe Choisy 1 Abdel Belaïd 1
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.
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Submitted on : Tuesday, September 26, 2006 - 8:47:39 AM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
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  • HAL Id : inria-00099041, version 1



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. ⟨inria-00099041⟩



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