# ICDAR 2021 Competition on On-Line Signature Verification

arXiv: https://arxiv.org/abs/2106.00739 · HTML: https://mlsanity.com/paper/2106.00739/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification](https://mlsanity.com/paper/1911.05358/) (2019)
- [Biometric Template Storage with Blockchain: A First Look into Cost and Performance Tradeoffs](https://mlsanity.com/paper/1904.13128/) (2019)
- [Exploiting Complexity in Pen- and Touch-based Signature Biometrics](https://mlsanity.com/paper/1905.03676/) (2019)

## Built on by (most cited first, 8 of 8)

- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification](https://mlsanity.com/paper/2210.08836/) (2022)
- [Neural network modelling of kinematic and dynamic features for signature verification](https://mlsanity.com/paper/2411.17506/) (2024)
- [On the use of first and second derivative approximations for biometric online signature recognition](https://mlsanity.com/paper/2406.00512/) (2024)
- [Explainable offline automatic signature verifier to support forensic handwriting examiners](https://mlsanity.com/paper/2405.12695/) (2024)
- [Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation](https://mlsanity.com/paper/2410.06041/) (2024)
- [Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models](https://mlsanity.com/paper/2503.13573/) (2025)
- [Online Signature Verification Using Augmented Path Signature and T-Mamba](https://mlsanity.com/paper/2609.08276/) (2026)

## Abstract (arXiv)

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols.