Large scale on-line handwritten Chinese character recognition using improved syntactic pattern recognition
Kazuhiro Kuroda, Koichi Harada, Masafumi Hagiwara · 2002
We propose an original method for the recognition of online handwritten Chinese characters using an improved syntactic pattern recognition. Syntactic pattern recognition is a method that converts a pattern into a string of symbols using a finite set of features and then analyzes them structurally using grammars. It is effective for such patterns as structurally constructed Chinese characters. 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 which express features of the successors, and are analyzed by simple calculations between matrices. Moreover in order to symbolize and analyze efficiently and accurately in a large scale, we employ a hierarchical approach for the proposed method. Using free writing characters, we obtained a 99.49% recognition rate for training patterns and 94.34% for test patterns.