Hierarchical models for analysis and recognition of handwritten characters |
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Authors: | Z C Li C Y Suen J Guo |
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Affiliation: | (1) Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan 80242 ROC;(2) Centre for Pattern Recognition and Machine Intelligence, Department of Computer Science, Concordia University, H3G 1M8 Montreal, Canada |
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Abstract: | Different hierarchical models in pattern analysis and recognition are proposed, based on occurrence probability of patterns. As an important application of recognizing handprinted characters, three typical kinds of hierarchical models such asM
89-89,M
89-36 andM
36-36 have been presented, accompanied by the computer algorithms for computing recognition rates of pattern parts. Moreover, a comparative study of their recognition rates has been conducted theoretically; and numerical experiments have been carried out to verify the analytical conclusions made. Various hierarchical models deliberated in this paper can provide users more or better choices of pattern models in practical application, and lead to a uniform computational scheme (or code). The recognition rates of parts can be improved remarkably by a suitable hierarchical model. For the modelM
89-36 in which case some of the Canadian standard handprinted characters have multiple occurrence probabilities, the total mean recognition rates of the given sample may reach 120% of that by the model proposed by Li et al., and 156% of that obtained from the subjective experiments reported by Suen. |
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