Offline handwritten signature verification — Literature review
Citations Over TimeTop 10% of 2017 papers
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.
Related Papers
- → A review of handwritten signature verification systems and methodologies(2016)10 cited
- → The Algorithm of On-Line Handwritten Signature Verification Based on Hide Markov Model(2009)3 cited
- → Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey(2014)2 cited
- Performance evaluation of handwritten signature verification techniques(2008)
- → Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey(2014)