Signature Verification on Bank Checks Using Hopfield Neural Network

The used topology i.e. Associative Hopfield Memory is a very useful form of Artificial Neural Networks. This paper outlines an optimization relaxation approach for signature verification based on the Hopfield neural network (HNN). The standard sample signature of the customer is cross matched with the one supplied on the check. The difference percentage is obtained by calculating the different pixels in both the images. The network topology is built so that each pixel in the difference image is a neuron in the network. Each neuron is categorized by its status, which in turn signifies that if the particular pixel is changed. The network converges to unwavering condition based on the energy function which is derived in experiments. The Hopfield's model allows each node to take on two binary state values (changed/unchanged) for each pixel. The performance of the proposed technique is evaluated by applying it in various binary and gray scale images. This paper contributes in finding an automated scheme for verification of authentic signature on bank check. The derived energy function allows a trade-off between the influence of its neighbourhood and its own criterion. This device is able to recall as well as complete partially specified inputs.