# Offline Signature Verification on Real-World Documents

arXiv: https://arxiv.org/abs/2004.12104 · HTML: https://mlsanity.com/paper/2004.12104/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Evaluating Synthetic Pre-Training for Handwriting Processing Tasks](https://mlsanity.com/paper/2304.01842/) (2023)
- [Anonymization of Documents for Law Enforcement with Machine Learning](https://mlsanity.com/paper/2501.07334/) (2025)
- [Fully-Automatic Pipeline for Document Signature Analysis to Detect Money Laundering Activities](https://mlsanity.com/paper/2107.14091/) (2021)
- [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)

## Abstract (arXiv)

Research on offline signature verification has explored a large variety of methods on multiple signature datasets, which are collected under controlled conditions. However, these datasets may not fully reflect the characteristics of the signatures in some practical use cases. Real-world signatures extracted from the formal documents may contain different types of occlusions, for example, stamps, company seals, ruling lines, and signature boxes. Moreover, they may have very high intra-class variations, where even genuine signatures resemble forgeries. In this paper, we address a real-world writer independent offline signature verification problem, in which, a bank's customers' transaction request documents that contain their occluded signatures are compared with their clean reference signatures. Our proposed method consists of two main components, a stamp cleaning method based on CycleGAN and signature representation based on CNNs. We extensively evaluate different verification setups, fine-tuning strategies, and signature representation approaches to have a thorough analysis of the problem. Moreover, we conduct a human evaluation to show the challenging nature of the problem. We run experiments both on our custom dataset, as well as on the publicly available Tobacco-800 dataset. The experimental results validate the difficulty of offline signature verification on real-world documents. However, by employing the stamp cleaning process, we improve the signature verification performance significantly.