An a priori indicator of the discrimination power of discrete hidden Markov models
An a priori indicator of the discrimination power of discrete hidden Markov models
Grandidier, F. and Sabourin, R. and Gilloux, M. and Suen, C. Y.
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 2001
Abstract : During the development of a hidden Markov modelbased handwriting recognition system, the testing phase takes a non-negligible amount of computation time. This is especially true for real application where the lexicon size is large. In order to shorten the development process we propose an indicator of the system discrimination power. This indicator is calculated during training and its final value is obtained at the end of the training phase, without more calculation. Its definition consists of a modification of the observation probability of the validation corpus by the trained system. Some experiments were carried out and the results show clearly the correlation between this indicator and recognition rates.
