Radical-based neighboring segment matching method for on-line Chinese character recognition

Abstract

A new approach to stroke-order and stroke-number free on-line handwritten Chinese character recognition is presented in this paper. In this new scheme, the decision rule of the segment attribute is used to characterize the segment sequence appearing in each Chinese character for recognizing connected-stroke and even cursive handwritten Chinese characters. A knowledge-based radical extraction method is proposed to perform the feature extraction before radical recognition stage. The top-level and bottom-level radical classification are adopted in the coarse classification stage to reduce the number of candidate characters. In order to develop a stroke order free system, the neighboring segment matching method is proposed. Experimental results show that the proposed scheme is an efficient solution for stroke-order and stroke-number free on-line Chinese character recognition. The recognition rate is 93.4% and the recognition speed is 0.6 second per character.