Offline Signature Recognition Using ANN

A person’s signature is an important vital feature for a person that can be used to verify human identity, so we used the artificial neural network method to recognize the signature, and it consists of simple elements that work in parallel, these elements are inspired by the biological nervous system. The principle of its work is that the signature is captured and presented to the user in a form picture. Signature verification can be classified into online signature verification and offline signature verification. Online verification is based on dynamic capturing of signatures when they are made whereas Offline verification generally uses a scanned image of signatures. The objective of this project is to focus on the offline model of verification where several signatures are put through various processes before finally verifying it to be true or forged through Artificial Neural Networks (ANN). . To perform verification or identification of a signature, several steps must be performed. These steps are: * Image pre-processing * Feature extraction * Neural Network Training From many algorithms and methods with different accuracy percentages In this project we propose a human signature recognition system based canny edge detection and pattern averaging and back propagation neural network system.