A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Off-Line Recognizer for Hand-Written Chinese Characters
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
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A hand-printed Chinese character recognizer based on contextual vector quantization (CVQ) has been built. The idea of CVQ is to quantize each pixel to a codeword by considering not just the pixel itself but its neighbors and their codeword identities as well. 100 samples of each character are collected from 100 writers, among them, 92 are used for training and 8 for testing. The characters are scanned by a 300 dpi scanner, which are then noise removed, thinned, segmented and size normalized. Stroke counts and segment strengths are adopted as observation features. For a vocabulary of 470 simplified Chinese characters, a recognition rate of 97% is achieved.