A BANK CHEQUE SIGNATURE VERIFICATION SYSTEM

Abstract

The aim of our work is to provide an offline signature authentication system that help bankers to verify whether the signature performed by the customer is genuine or forger. The point that the sign is extensively used as resource for person verification stresses the necessity for a verification system. There are two major ways to perform verification that are following: • Online systems • Offline systems Both systems vary in various aspects. If we talk about online systems so one thing is common in all online systems that all of these process use active aspects of a signature that is captured during the moment signatures are performed by the individual. On the other hand, scanned image is the essential element in offline system that makes it to perform work. Our group work objective is to achieve an offline sign verification/authentication that is based on transfer learning. This report consists of different techniques that we have applied in order to provide best accuracy results. Techniques and steps like data acquisition, pre-processing, feature extraction, classification and verification is explained in detail. Furthermore, inception v3 and mobile Net are the major highlighting features of our project, these enhances accuracy of the result by using different layers in order to recognize other scanned images of the signatures performed. The advantage of using these features with transfer learning is this that inception v3 is playing a major role over many years in order to provide best accuracy result by working on image dataset features that are extracted. Moreover, modelling and training is also part of this system. Training is performed by softmax layer that is assumed to be normalized as N + 2048*N (or J001*N) model parameters. Beside this all, report also consists of final recommendation and conclusion that is : consisting ofreasons to use all ofthe above techniques.