OFF-LINE RECOGNITION OF HANDWRITTEN CHINESE CHARACTERS BY ONLINE MODEL-GUIDED MATCHING

Abstract

A model-guided structural matching method for recognizing handwritten off-line Chinese characters is presented. According to the stroke writing sequence, each character is described by an on-line model, which is a one-dimensional (1D) string consisting of a stroke sequence interleaved with relationships between two consecutive strokes. After an unknown input is thinned and line approximated, all possible strokes are extracted. A tree is created to represent all possible combinations of the extracted strokes which have the same stroke sequence as the one defined in the model. In the recognition process, we match the strokes with those defined in the on-line model. The matching process is formulated as a tree searching algorithm guided by the relationships in the model. The modelled character is taken to be a candidate if there is a feasible path which satisfies the relationships defined in the on-line model and the number of missing strokes in the input is less than a given threshold. The input is recognized as the character with the greatest number of matched strokes among all candidates. Experimental results on 300 frequently used characters in a database called CCL/HCCR1, which contains 5401 Chinese characters and about 250 variations for each one, show that the recognition rate is over 90%.