Offline Handwritten Signature Verification - Literature Review

TL;DR

This literature review examines the evolution and recent advancements in offline handwritten signature verification, highlighting the impact of deep learning techniques and outlining future research directions.

Contribution

It provides a comprehensive overview of past methods, recent deep learning innovations, and suggests potential future research avenues in offline signature verification.

Findings

Deep learning has significantly improved feature extraction.

Recent methods outperform traditional approaches.

Future research should explore multimodal and explainable models.

Abstract

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.