Large-set handwritten character recognition with multiple stochastic models

Hee-Seon Park, Seong-Whan Lee

Abstract: An efficient recognition scheme for large-set handwritten characters is proposed in the framework of multiple stochastic models, in this case, first order hidden Markov models which can model stochastically the input pattern with numerous variations. In this scheme, after extracting four kinds of regional projection contours for an input pattern by using the regional projection contour transformation, four kinds of HMMs are constructed during the training phase based on the direction components of these contours. In the recognition phase, the four kinds of HMMs constructed in the training phase are combined to output the final recognition result for an input pattern.<