A Stroke Shape and Structure Based Approach for Off-line Chinese Handwriting Identification

Abstract

Handwriting identification is a technique of automatic person identification based on the personal handwriting. It is a hot research topic in the field of pattern recognition due to its indispensible role in the biometric individual identification. Although many approaches have emerged, recent research has shown that off-line Chinese handwriting identification remains a challenge problem. In this paper, we propose a novel method for off-line Chinese handwriting identification based on stroke shapes and structures. To extract the features embedded in Chinese handwriting characters, two special structures have been explored according to the trait of Chinese handwriting characters. These two structures are the bounding rectangle and the TBLR quadrilateral. Sixteen features are extracted from the two structures, which are used to compute the unadjusted similarity, and the other four commonly used features are also computed to adjust the similarity adaptively. The final identification is performed on the similarity. Experimental results on the SYSU and HanjaDB1 databases have validated the effectiveness of the proposed method.