A Prototype System For Off-Line Signature Verification Using Multilayered Feedforward Neural Networks

Signatures are used everyday to authorise the transfer of funds of millions of people. Bank checks, credit cards and legal documents all require our signatures. Forgeries in such transactions cost millions of dollars each year. By forgery is meant copying, falsifying, or altering any kind of written or printed matter for the purpose of defrauding others. Signature verification is the process carried out to determine whether a given signature is genuine or forged. There are two major methods of signature verification. One is an on-line method to measure the sequential data such as handwriting and pen pressure with a special device. The other is an off-line method that uses an optical scanner to obtain handwriting data written on paper. This research investigated the suitability of using multilayered feedforward neural networks for the task of off-line signature verification. The emphasis was on investigating the performance of the networks when presented with raw signature images and no..