OFF-LINE Signature Verification Using Neural Network Approach

Signature verification is the process carried out to determine whether a given signature is genuine or forged. Handwriting comes in many different forms and there is great deal of variability even signature of people that use same language. Some signature may be quite complex while others are simple and appear as if they may be forged easily. In this paper we present an effective method to perform off-line signature verification and identification. First of all the signatures are converted into .PBM format so that less information to process. Then different feature extraction methods are used to obtain optimized high performance signature verification for improving the identification rate. Two different files are used one to train the network and another to test the network. Finally neural network Radial basis function network (RBF) is used as classifier. RBF provides better verification accuracy than any other classifier.