ONLINE RECOGNITION BY DEVIATION-EXPANSION MODEL AND DYNAMIC-PROGRAMMING MATCHING

An on-line recognition system is presented for large-alphabet handprinted Chinese characters by the model based recognition approach with stroke based features. A deviation-expansion (D-E) model representing the reference pattern is constructed. The model contains the hypothetical knowledge of handwriting variations including stroke-order deviations and stroke-number deviations. For pattern matching a matching graph is constructed by combining the knowledge of the reference pattern and the unknown pattern together. With the graph a similarity measure function is defined to indicate similarity degree, The evaluation of the function is obtained by utilizing dynamic programming matching. Experimental results are based upon the testing set of 54,000 handprinted sample characters written in square style by ten persons. The D-E models of reference patterns saved in a data base are generated by 5400 daily-used Chinese characters. The unknown character to be recognized can be stroke-order and stroke-number free, tolerant for incorrect strokes and daily-used connected strokes, size and shape flexible. The cumulative classification rate of choosing the ten most similar characters is 98%. The results suggest that the hypothetical model is feasible and reasonable.