Enhancing the Authentication of Bank Cheque Signatures by Implementing Automated System Using Recurrent Neural Network

TL;DR

This paper proposes an automated signature verification system for bank cheques using a Hopfield neural network, which compares signature images by pixel differences and utilizes an energy function for stable pattern recognition.

Contribution

It introduces a novel application of Hopfield neural networks for signature verification, optimizing pattern matching through an energy-based approach.

Findings

Effective in binary and grayscale images

Achieves stable pattern recognition

Automates signature authentication process

Abstract

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.