Learning approach for offline signature verification using vector quantization technique

Abstract

Signature is a behavioral trait of an individual and forms a special class of handwriting in which legible letters or words may not be exhibited. Signature Verification System (SVS) can be classified as either offline or online. [1] In this paper, we used vector quantization technique for signature verification. The data is captured at a later time by using an optical scanner to convert the image into a bit pattern. The features thus extracted are said to be static. Our system is designed using cluster based features which are modeled using vector quantization as its density matching property provides improved results compared to statistical techniques. The classification ratio achieved using Vector Quantization is 67%.