Online recognition by deviation-expansion model and A^* algorithm-based matching

This paper presents an online recognition system for large-alphabet handprinted Chinese characters using a model-based recognition approach with stroke-based features. A deviation-expansion (D-E) model representing the reference pattern is constructed. The model contains hypothetical knowledge of handwriting variations, including stroke-order deviations and strokenumber deviations. For pattern matching a matching tree is constructed by combining the knowledge of the reference pattern and the unknown pattern together. With the tree a similarity measure function is defined to indicate the degree of similarity. Evaluation of the function is obtained using A^* algorithm-based matching. Experimental results are based upon testing a set of 54010 handprinted sample characters written in the square style by ten people. The cumulative classification rate of choosing the ten most similar characters is 98%. The results suggest that the hypothetical model is both feasible and reasonable.