# MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

arXiv: https://arxiv.org/abs/2210.08836 · HTML: https://mlsanity.com/paper/2210.08836/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.05787/) (2017)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [UTSig: A Persian Offline Signature Dataset](https://mlsanity.com/paper/1603.03235/) (2016)
- [BioTouchPass: Handwritten Passwords for Touchscreen Biometrics](https://mlsanity.com/paper/2205.01353/) (2022)
- [A Preliminary Study on Aging Examining Online Handwriting](https://mlsanity.com/paper/2203.03933/) (2022)

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

- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding](https://mlsanity.com/paper/2602.12957/) (2026)
- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach](https://mlsanity.com/paper/2412.11668/) (2024)
- [Capturing More: Learning Multi-Domain Representations for Robust Online Handwriting Verification](https://mlsanity.com/paper/2508.01427/) (2025)
- [Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification](https://mlsanity.com/paper/2501.11895/) (2025)
- [Telling Human and Machine Handwriting Apart](https://mlsanity.com/paper/2601.11700/) (2026)

## Abstract (arXiv)

Although online handwriting verification has made great progress recently, the verification performances are still far behind the real usage owing to the small scale of the datasets as well as the limited biometric mediums. Therefore, this paper proposes a new handwriting verification benchmark dataset named Multimodal Signature and Digit String (MSDS), which consists of two subsets: MSDS-ChS (Chinese Signatures) and MSDS-TDS (Token Digit Strings), contributed by 402 users, with 20 genuine samples and 20 skilled forgeries per user per subset. MSDS-ChS consists of handwritten Chinese signatures, which, to the best of our knowledge, is the largest publicly available Chinese signature dataset for handwriting verification, at least eight times larger than existing online datasets. Meanwhile, MSDS-TDS consists of handwritten Token Digit Strings, i.e, the actual phone numbers of users, which have not been explored yet. Extensive experiments with different baselines are respectively conducted for MSDS-ChS and MSDS-TDS. Surprisingly, verification performances of state-of-the-art methods on MSDS-TDS are generally better than those on MSDS-ChS, which indicates that the handwritten Token Digit String could be a more effective biometric than handwritten Chinese signature. This is a promising discovery that could inspire us to explore new biometric traits. The MSDS dataset is available at https://github.com/HCIILAB/MSDS.