# Offline Handwritten Signature Verification - Literature Review

arXiv: https://arxiv.org/abs/1507.07909 · HTML: https://mlsanity.com/paper/1507.07909/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2015.

## Built on by (most cited first, 15 of 15)

- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2201.10138/) (2022)
- [SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2202.13078/) (2022)
- [Offline Handwritten Signature Verification Using a Stream-Based Approach](https://mlsanity.com/paper/2411.06510/) (2024)
- [Fully-Automatic Pipeline for Document Signature Analysis to Detect Money Laundering Activities](https://mlsanity.com/paper/2107.14091/) (2021)
- [Writer Recognition Using Off-line Handwritten Single Block Characters](https://mlsanity.com/paper/2201.10665/) (2022)
- [Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder](https://mlsanity.com/paper/2409.19754/) (2024)
- [Revealing Reliable Signatures by Learning Top-Rank Pairs](https://mlsanity.com/paper/2203.09927/) (2022)
- [Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.01136/) (2023)
- [Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification](https://mlsanity.com/paper/2312.00987/) (2023)
- [Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations](https://mlsanity.com/paper/2401.03085/) (2024)
- [Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures](https://mlsanity.com/paper/2405.14409/) (2024)
- [Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation](https://mlsanity.com/paper/2410.06041/) (2024)
- [Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer](https://mlsanity.com/paper/2510.19321/) (2025)
- [ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles](https://mlsanity.com/paper/2605.07816/) (2026)

## Abstract (arXiv)

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.