Offline Chinese Signature Verification Based on Segmentation and RBFNN Classifier

A simple, low computational cost and robust segmentation method is proposed by means of having successful experiences of strokes extraction for handwritten Chinese character and taking into account the characteristics of signature verification. After segmented and feature extracted, each signature is represented by a series of 6-dimensions vectors. Then, the degree of similarity between the questioned sample and 4 genuine signature samples stored in the reference database is calculated using these vectors. At last, the similarity vectors are inputted into RBFNN Classifier to decide whether the question sample is a genuine sample or not. The promising results of experiments indicate the segmentation method is fitting for Chinese signature verification and the whole verification method distinguish forgeries from genuine signatures effectively.