Feature selection in recognition of handwritten Chinese characters

Abstract

Recognition of handwritten Chinese characters is a large-scale pattern recognition task, which is difficult and time consuming to build the corresponding classifiers. In this paper, two feature selection methods are proposed to reduce the complexity and speed up the handwritten Chinese recognition: one is the ReliefF-Wrapper method which evaluates the original features with the ReliefF method, and then uses the wrapper method to decide the number of features to be selected; and the other is GA-Wrapper that uses genetic algorithm to search the optimal subset of features with high training accuracy. Experiments were performed on 800 most frequently used Chinese characters, with 80,000 handwritten samples. Results show that the ReliefF-Wrapper method has good interpretation and high speed and GA-Wrapper gains higher accuracy. Limitations of the both methods and future work are also discussed.