Adaptive discrimination in an HMM-based neural predictive system for on-line word recognition

We have introduced previously (1996) a neural predictive system for on-line word recognition. Our approach implements a hidden Markov model (HMM)-based cooperation of several neural networks. The task of the HMM is to guide the training procedure of neural networks on successive parts of a word. Each word is modeled by the concatenation of letter-models corresponding to the letters composing it. In this article, we present the discriminative training procedures introduced in order to improve the results of our first model. Discriminative training is described at the local level, that is of each extracted parameter vector, and at the global level, that is the level of sequences of labels. We relate this type of training in both cases to the maximum mutual information formalism. Discriminative training was performed on 7000 words from 9 writers, leading to improved results at the character level. Moreover, the use of a neural lexical post-processor (NLPP) gives very good word recognition rates.