  The associatie memory feature of the Hopfield type recurrent neural network
is used for the pattern storage and pattern authentication.This paper outlines
an optimization relaxation approach for signature verification based on the
Hopfield neural network (HNN)which is a recurrent network.The standard sample
signature of the customer is cross matched with the one supplied on the
Cheque.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 states,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 Cheques.The derived energy function allows a
trade off between the influence of its neighborhood and its own criterion.This
device is able to recall as well as complete partially specified inputs.The
network is trained via a storage prescription that forces stable states to
correspond to (local)minima of a network "energy" function.
