# A Prototypical Signature Approach for Writer-Independent Offline Signature Verification

arXiv: https://arxiv.org/abs/2606.18528 · HTML: https://mlsanity.com/paper/2606.18528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [Sample Selection for Fair and Robust Training](https://mlsanity.com/paper/2110.14222/) (2021)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Offline Handwritten Signature Verification Using a Stream-Based Approach](https://mlsanity.com/paper/2411.06510/) (2024)
- [Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder](https://mlsanity.com/paper/2409.19754/) (2024)

## Abstract (arXiv)

Offline handwritten signature verification aims to distinguish genuine from forged signatures using static images. Since real forgeries are rarely available, negative samples are usually randomly drawn from genuine signatures of other users to create training data. However, this random selection often lacks diversity, increases redundancy, and escalates computational cost, leading to inefficient training. We propose a data-driven strategy to generate diverse, informative negative samples using prototypical signatures, which are compact, non-identifiable summaries of genuine signature features. Based on the experiments results, we conclude that (i) prototypical signatures yield more informative negative samples, improving the detection of skilled forgeries; (ii) the proposed approach is backbone-agnostic, showing robustness across architectures; and (iii) when combined with a primal-form linear SVM, it serves as an alternative to RBF-based models while significantly improving scalability and computational efficiency. Implementation of the method is available at https://github.com/kdmoura/proto_hsv.