Context-dependent substroke model for HMM-based on-line handwriting recognition
Context-dependent substroke model for HMM-based on-line handwriting recognition
复制标题
基于 HMM 的在线手写识别的上下文相关子笔画模型
DOI：
10.1109/iwfhr.2002.1030888
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
S. Sagayama
中科院分区：
文献类型：
--
作者：
Jun;Nobuhito Inami;Shigeki Matsuda;M. Nakai;H. Shimodaira;S. Sagayama
Describes context-dependent substroke hidden Markov models (HMMs)for on-line handwritten recognition of cursive Kanji and Hiragana characters. In order to tackle this problem, we have proposed the substroke HMM approach where a modeling unit "substroke" that is much smaller than a whole character is employed and each character is modeled as a concatenation of only 25 kinds of substroke HMMs. One of the drawbacks of this approach is that the recognition accuracy deteriorates in the case of scribbled characters, and characters where the shape of the substrokes varies a lot. We show that the context-dependent substroke modeling which depends on how the substroke connects to the adjacent substrokes is effective for achieving robust recognition of low quality characters, The successive state splitting algorithm which was mainly developed for speech recognition is employed to construct the context dependent substroke HMMs. Experimental results show that the correct recognition rate improved from 88% to 92% for cursive Kanji handwriting and from 90% to 98% for Hiragana handwriting.
