A FUZZY MODEL FOR UNSUPERVISED CHARACTER CLASSIFICATION

This paper presents a fuzzy logic approach to efficiently perform unsu pervised character classification for improvement in robustness, corre ctness, and speed of a character recognition system. The characters ar e first split into seven typographical categories. The classification scheme uses pattern matching to classify the characters in each catego ry into a set of fuzzy prototypes based on a nonlinear weighted simila rity function. The fuzzy unsupervised character classification, which is natural in the representation of prototypes for character matching, is developed and a weighted fuzzy similarity measure is explored. The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character re cognition which is simply applied on a smaller set of the fuzzy protot ypes, becomes much easier and less time-consuming.