Offline signature recognition and verification based on artifical neural network

Abstract EN

In this paper, a problem for Offline Signature Recognition and Verification is presented.

A system is designed based on two neural networks classifier and three powerful features (global, texture and grid features).

Our designed system consist of three stages : the first is preprocessing stage, second is feature extraction stage and the last is neural network (classifiers) stage which consists of two classifiers, the first classifier consists of three Back Propagation Neural Network and the second classifier consists of two Radial Basis Function Neural Network.

The final output is taken from the second classifier which decides to whom the signature belongs and if it is genuine or forged.

The system is found to be effective with a recognition rate of (% 95.955) if two back propagation of the first classifier recognize the signature and (% 99.31) if all three back propagation recognize the signature.