Large scale on-line handwritten Chinese character recognition using successor method based on stochastic regular grammar

In this paper, we propose an original method for the recognition of on-line handwritten Chinese characters using the successor method based on the stochastic regular grammar. We use Kohonen's self-organizing feature map for feature extraction to get optimal sets of prototypical waveforms of peaks from sample data automatically. The strings of symbols are converted into matrices using the stochastic successor method, and are analyzed by simple calculation between matrices. In order to symbolize and analyze input patterns efficiently and accurately in a large scale, we employ a hierarchical approach. Using unrestricted handwritten characters, we obtained 94.34% recognition rate for the test patterns.