A comparative study of signature recognition problem using statistical features and artificial neural networks

This paper summarizes a research effort for an off-line signature recognition system. Neural network is used to address this problem because the learning and generalization abilities of NNs enable them to cope up with the diversity and the variation of human signatures. Since neural network have proven performance in other pattern recognition tasks such as character recognition therefore, it is equally suitable for the task of signature recognition. In this paper we present a comparative study of signature recognition comprises of three different neural networks i.e., feed-forward-back propagation neural network, competitive and probabilistic neural network. We have proved by experiment and analysis that probabilistic neural network is best suited to deal with signature recognition problem with an average of 100% accuracy.