Offline Handwritten Signature Verification - Literature Review
This is a literature review, so it is incremental, summarizing existing work in a domain-specific area of biometric security.
This paper reviews the problem of offline handwritten signature verification, which aims to distinguish genuine signatures from forgeries using scanned images, and analyzes recent advancements, particularly the application of deep learning methods, along with future research directions.
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.