Off-line signature verification and recognition by Support Vector Machine

In this paper we present an off-line signature verification and recognition system using the global, directional and grid fea-tures of signatures. Support Vector Machine (SVM) was used to verify and classify the signatures and a classification ratio of 0.95 was obtained. As the recognition of signatures repre-sents a multiclass problem SVM's one-against-all method was used. We also compare our methods performance with Artifical Neural Network's (ANN) backpropagation method.