Global feature for online character recognition
Pattern Recognition Letters
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This paper presents a new approach to online recog- nition of handwritten Kanji characters focusing on their hierarchical structure. Stochastic context-free grammar (SCFG) is introduced to represent the Kanji character gen- erating process in combination with Hidden Markov Mod- els (HMM) representing Kanji substrokes and to improve the recognition accuracy of important and frequently used Kanji characters in which inter-stroke relative positions play important roles. Combining the stroke likelihood and the relative-position likelihood between character-parts in the parsing process is expected to compensate their ambi- guities. By modeling relative positions and share the models across distinct Kanji categories, a small training data can yield effective results and enables us to recognize Kanji sim- ply by defining the SCFG rules to represent their structures without training data. Experimental results on an online handwritten Kanji database from JAIST (Japan Advanced Institute of Science and Technology) showed significant im- provements in the recognition rates of some important Kanji with relatively fewer strokes and also showed little differ- ence between the trained- and the non-trained Kanji in recognition rates.