| 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 Algorithm. |
In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 487-492.