Analytic word recognition without segmentation based on Markov random fields

C. Coisy, Abdel Belaı̈d · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2004

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 NSHP­HMM (Markov field). Global models are build dynamically, and used for recognition and learning with the Baum­Welch 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 dur­ ing learning. First experiments on a real base of french check amount words give encouraging results of 83.4% for recognition. Keywords : HMM, NSHP­HMM, Cross­learning, Meta­models, Baum­Welch Algori

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