On machine recognition of hand-printed Chinese characters by feature relaxation

Abstract A new relaxation matching method based on features is introduced for the recognition of hand-printed Chinese characters. The types of features are selected carefully to reflect the structural information of characters. Matching probabilities between two features, one from the mask and the other from input, are computed by the relaxation method. A new distance measure between two characters based on these matching probabilities is defined. We demonstrate, through examples, the utility of the new approach in the recognition of hand-printed Chinese characters. It is especially powerful in distinguishing similarly-shaped characters within a cluster produced by preclassification.