Local Contour Features for Writer Identification
Hong Ding, Huiqun Wu, Xiaofeng Zhang · Advanced science and technology letters · 2013
A method based on local contour features is proposed for writer identiflcation in this paper. In preprocessing, an improved Bern- son algorithm is used to abstract contours form images. Then the distri- bution of local contour is extracted from the fragments which are parts of the contour in sliding windows. In order to reduce the impact of stroke weight, the fragments which do not directly connect the center point are ignored during feature abstraction. The edge point distributions of the fragments are counted and normalized into Local Contour Distribution Features (LCDF). At last, weighted Manhattan distance is used as sim- ilarity measurement. The experiments on our database show that the performances of the proposed method gets the state-of-art performance.