A fuzzy model for unsupervised character classification

Abstract This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness, and speed of a character recognition system. The characters are first split into seven typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity 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 recognition which is simply applied on a smaller set of the fuzzy prototypes, becomes much easier and less time-consuming.