# Offline Handwritten Signature Verification Using a Stream-Based Approach

arXiv: https://arxiv.org/abs/2411.06510 · HTML: https://mlsanity.com/paper/2411.06510/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [Offline Handwritten Signature Verification - Literature Review](https://mlsanity.com/paper/1507.07909/) (2015)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [A writer-independent approach for offline signature verification using deep convolutional neural networks features](https://mlsanity.com/paper/1807.10755/) (2018)
- [Standardized Evaluation of Machine Learning Methods for Evolving Data Streams](https://mlsanity.com/paper/2204.13625/) (2022)
- [The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited](https://mlsanity.com/paper/1304.6383/) (2013)

## Built on by (most cited first, 2 of 2)

- [Quasi-Synthetic Riemannian Data Generation for Writer-Independent Offline Signature Verification](https://mlsanity.com/paper/2509.20420/) (2025)
- [A Prototypical Signature Approach for Writer-Independent Offline Signature Verification](https://mlsanity.com/paper/2606.18528/) (2026)

## Abstract (arXiv)

Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to various factors, including time and external influences, imparting them a dynamic nature. This paper investigates the signature learning process within a data stream context. We propose a novel HSV approach with an adaptive system that receives an infinite sequence of signatures and is updated over time. Experiments were carried out on GPDS Synthetic, CEDAR, and MCYT datasets. Results demonstrate the superior performance of the proposed method compared to standard approaches that use a Support Vector Machine as a classifier. Implementation of the method is available at https://github.com/kdMoura/stream_hsv.