Off-line Chinese signature verification based on support vector machines

This paper proposes a novel off-line Chinese signature verification method based on support vector machines. The method uses both static features and dynamic features. The static features include moment features and 16-direction distribution (an improvement on 4-direction distribution). The dynamic features include gray distribution and stroke width distribution. At last, support vector machine is used to classify the signatures. The main steps of constructing a signature verification system are discussed and experiments on real data sets show that the average error rate can reach 5%, which is obviously satisfactory.