Adaptation de modèles de Markov cachés - Application à la reconnaissance de caractères imprimés

International audienceWe present in this paper a new algorithm for the adaptation of hidden Markov models (HMM models). The principle of our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation stage. This algorithm is applied to the recognition of printed characters to adapt the models learned by a polyfont character recognition engine to new forms of characters. Comparing the results with those of MAP and MLLR classic adaptations shows a slight increase in the performance of the recognition system