# Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature

arXiv: https://arxiv.org/abs/1705.06849 · HTML: https://mlsanity.com/paper/1705.06849/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2017.

## Built on by (most cited first, 7 of 7)

- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [Signature Verification using Geometrical Features and Artificial Neural Network Classifier](https://mlsanity.com/paper/2108.02029/) (2021)
- [1-D CNN-Based Online Signature Verification with Federated Learning](https://mlsanity.com/paper/2406.06597/) (2024)
- [Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer](https://mlsanity.com/paper/2510.19321/) (2025)
- [Online Signature Verification Using Augmented Path Signature and T-Mamba](https://mlsanity.com/paper/2609.08276/) (2026)

## Abstract (arXiv)

Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification. LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification. Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).