Classification of handprinted Chinese characters using nonlinear normalization and correlation methods

Abstract

A description is given of the classification of handprinted Chinese characters, using correlation methods for fast classification and a nonlinear normalization based on uniform relocation of the strokes used to form the character. Experimental results for handprinted Chinese character classification are presented. High classification capability, at 97.36% for the recognition rate and 99.44% for the rough classification was achieved for a large data set (ETL8), which includes 881 handprinted Chinese characters and 160 character patterns per character. A 94.42% recognition rate was achieved for a larger data set (ETL9B), which includes 2965 handprinted Chinese characters.