Offline Handwritten Chinese Character Recognition Using Genetic Algorithm and Rough Set

This paper will discuss about combination of the genetic algorithm and the rough set theory to solve complex, multi-class super-deformed and multi-pattern recognition problems of the offline handwritten Chinese character recognition. It gives a genetic algorithm based offline handwritten Chinese character feature simple algorithm, without loss of the original information, reducing the feature vector dimension, reducing the complexity of the recognition processing. It also presents a heuristic method of redundancy reduction samples and reduction redundant training samples, to further reduce the complexity of the recognition processing. It proposes a rule-based confidence offline handwritten Chinese character integration recognition rule, the experimental results show that the proposed feature reduction method of the reduction effect of the multidimensional statistical features of offline handwritten Chinese character is obvious; rule confidence fusion recognition method can improve the Recognition rate of off-line handwritten Chinese character recognition system.