Structure Adaptation of HMM Applied to OCR

In this paper we present 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 of the parameters. This algorithm is applied to the recognition of printed characters to adapt the character models of a poly font general purpose character recognizer to new fonts of characters, never seen during training. A comparison of the results with those of MAP classical adaptation scheme show a slight increase in the recognition performance.

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