A Robust Authentication System Handwritten Documents using Local Features for Writer Identification

Abstract

A robust authentication system for handwritten document images carries considerable importance. The authenticity of legal documents, such as wills, signatures, etc., needs to be verified, in order to prevent fraudulent acts. The characteristics of writing style vary from person to person; by analyzing the local features of handwriting, we can possibly identify the writer. In this paper, we introduce a local feature based approach, in which writer-specific characteristics are extracted. The presence of repetitive patterns within handwriting gave us the idea of dividing the writing into a large number of small sub-images. The similarly shaped sub-images are grouped together in classes. The most repetitive patterns are extracted, and the unknown author of the document is identified, using a Bayesian classifier. We tested the system on 50 documents, achieving a successful identification rate of approximately 94%. Category: Embedded computing