On Line Isolated Characters Recognition Using Dynamic Bayesian Networks

In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of themajor interests of these systems resides in the complete training of the models (topology and parameters) starting fromtraining data. The representation of knowledge bases on description, by graphs, relations of causality existing between thevariables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networksto the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing ofthe structure which obliges us to admit some strong assumptions (for example independence between some variables