OFFLINE SIGNATURE IDENTIFICATION USING HIGH INTENSITY VARIATIONS AND CROSS OVER POINTS BASED FEATURE EXTRACTION

Abstract

Signature has its own advantage in person identification. The facts that people usually do not putting text in it; rather they draw a pattern as their signature. Even today, numbers of transactions are increasing related to banking and businesses are being identified via signatures. The main difficulty lies in the variations of the geometrical representation of the signature which is closely related to the identity of human beings. Hence, development methods for genuine signature verification must be needed. When bundles of documents, e.g. bank cheques, have to be verified in a limited time, the manual verification of account holders’ signatures is often tedious work. So there is a need of Automatic Signature Verification and Identification systems. For that different logic should be considered to process such signatures. The present paper is done in the field of offline signature identify by extracting some special domain features that make a signature difficult to forge. In this paper existing signature verification systems have been thoroughly studied and a model is designed to develop an offline signature idenfication system. Here off-line signature idenfication system that depends on high intensity variation based features as well as cross over points based features. Main aim is to take various feature points of a given signature and compares them with the test signatures feature points by choosing appropriate classifiers.