Online Handwritten Kanji Recognition Based on Inter-stroke Grammar

Abstract

This paper presents a new approach to online recognition of handwritten Kanji characters focusing on their hierarchical structure. Stochastic context-free grammar (SCFG) is introduced to represent the Kanji character generating process in combination with Hidden Markov Models (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 ambiguities. 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 simply 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 improvements in the recognition rates of some important Kanji with relatively fewer strokes and also showed little difference between the trained- and the non-trained Kanji in recognition rates.