Offline Handwritten Signature Verification - Literature Review

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.

Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017)

References in corpus (3)

• Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
• Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
• Analyzing features learned for Offline Signature Verification using Deep CNNs

Cited by in corpus (14)

• Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
• A Perspective Analysis of Handwritten Signature Technology
• Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
• Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
• Analyzing features learned for Offline Signature Verification using Deep CNNs
• Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs
• Handwritten Signature Verification Using Hand-Worn Devices
• Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
• Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures
• Graph-Based Offline Signature Verification
• Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti
• Active Transfer Learning for Persian Offline Signature Verification
• Fully-Automatic Pipeline for Document Signature Analysis to Detect Money Laundering Activities
• A comprehensive study of sparse representation techniques for offline signature verification