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📝MSDS Dataset

SCUT DLVC Lab Static Badge Static Badge Static Badge Static Badge

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

Neural Information Processing Systems (NeurIPS) Spotlight, 2022

⭐Official release of the MSDS dataset.

Directory

🖥️Usage & Download

The MSDS dataset is available for non-commercial research purposes only. To request access, please follow these steps:

Step 1: Download and complete both agreement documents:

Have both documents signed and stamped by your institution. Please also prepare 1–2 recent publications (within the last 6 years) as evidence that you or your team conduct research in handwriting verification, handwriting analysis and recognition, document image processing, or related fields.

Step 2: Submit your application online:

🔗 SCUT DLVC Lab Dataset Access Portal → Apply for MSDS

Upload both signed documents through the portal. Your application will be reviewed manually and you will be notified by email once a decision has been made (typically within 3–5 business days).

Step 3: Download the dataset:

After approval, you will receive the download link and decompression password via email.

⚠️ All users must comply with the use conditions at all times; failure to do so will result in revocation of access.

📖Description

MSDS dataset is a handwriting verification benchmark dataset and consists of two subsets: MSDS-ChS (for Chinese signatures) and MSDS-TDS (for Token Digit Strings). Each subset contains 16080 samples from 402 users, with 20 genuine samples and 20 skilled forgeries per user. The details are presented below:

Subset Content Online Offline User Genuine Sample Skilled Forgery Features
MSDS-ChS Chinese Signature ✓ ✓ 402 $402\times(10 + 10) = 8040$ $402\times(10 + 10) = 8040$ $X,Y,P,T,I_r,U$
MSDS-TDS Token Digit String ✓ ✓ 402 $402\times(10 + 10) = 8040$ $402\times(10 + 10) = 8040$ $X,Y,P,T,I_r,U$

$X,Y,P,T,I_r,U$ respectively denote the $x, y$ coordinates, pressure, timestamps, rendered static images, and the pen-up/pen-down information. The pen-down/pen-up information is represented by 0~4. 0 indicates that this is not a pen-up/pen-down point. 1 indicates that this is a pen-down point. 2 indicates that this is a pen-up point. 3 indicates that this point is both a pen-up and pen-down point, which is isolated.

The contributions of MSDS include:

  • MSDS-ChS is the largest publicly available Chinese signature dataset for signature for signature verification, at least eight times larger than existing ones.
  • MSDS-TDS is the first dataset that covers Token Digit String, which brings a new and effective biometric for handwriting verification.
  • The experimental results indicates that the Token Digit String is more powerful than Chinese signature, which is inspiring and promising.

🧬Collection

The data of MSDS was acquired with two types of Android tablets. Both of them have specific stylus. We specifically developed an Android app and the user interface is shown below. Users directly performed handwriting on the tablets with specific styluses and the produced information was automatically recorded by the app.

gui

The data acquisition process is divided into two separate sessions with a time interval of at least 21 days. In each session, users performed writing according to the same procedure: 10 genuine signatures→10 genuine phone numbers→10 forged signatures→10 forged phone numbers.

⚒️Responsible Use

MSDS is collected for handwriting identity verification. Specifically, the MSDS-ChS subset could be exploited in online/offline Chinese signature verification, and the MSDS-TDS subset is intended to be used in online/offline identity verification with Token Digit Strings. In addition, MSDS can be exploited in writer identification.

🔭Experimental Result

chs

Experimental results show all models perform better on MSDS-TDS than MSDS-ChS. This finding is inspiring that the accuracy of TDS verification is higher than that of Chinese signature verification as the two subsets were collected simultaneously. Therefore, Token Digit String could be a more effective biometric trait than Chinese signature for high-accurate online verification.

📄License

MSDS should be used and distributed under Creative Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License for non-commercial research purposes.

📁Directory Format

The dataset is organized in the following directory format:

├─MSDS
│  ├─MSDS-ChS
│  │  ├─session1
│  │  │  ├─0
│  │  │  │  ├─images
│  │  │  │  │  ├─f_0_0.png
│  │  │  │  │  ├─f_0_1.png
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.png
│  │  │  │  │  ├─g_0_1.png
│  │  │  │  │  └─...
│  │  │  │  └─series
│  │  │  │  │  ├─f_0_0.txt
│  │  │  │  │  ├─f_0_1.txt
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.txt
│  │  │  │  │  ├─g_0_1.txt
│  │  │  │  │  └─...
│  │  │  ├─1
│  │  │  │  ├─images
│  │  │  │  │  ├─f_0_0.png
│  │  │  │  │  ├─f_0_1.png
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.png
│  │  │  │  │  ├─g_0_1.png
│  │  │  │  │  └─...
│  │  │  │  └─series
│  │  │  │  │  ├─f_0_0.txt
│  │  │  │  │  ├─f_0_1.txt
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.txt
│  │  │  │  │  ├─g_0_1.txt
│  │  │  │  │  └─...
│  │  │  └─...
│  │  └─session2
│  │      ├─...
│  └─MSDS-TDS
│  │  ├─session1
│  │  │  ├─0
│  │  │  │  ├─images
│  │  │  │  │  ├─f_0_0.png
│  │  │  │  │  ├─f_0_1.png
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.png
│  │  │  │  │  ├─g_0_1.png
│  │  │  │  │  └─...
│  │  │  │  └─series
│  │  │  │  │  ├─f_0_0.txt
│  │  │  │  │  ├─f_0_1.txt
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.txt
│  │  │  │  │  ├─g_0_1.txt
│  │  │  │  │  └─...
│  │  │  ├─1
│  │  │  │  ├─images
│  │  │  │  │  ├─f_0_0.png
│  │  │  │  │  ├─f_0_1.png
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.png
│  │  │  │  │  ├─g_0_1.png
│  │  │  │  │  └─...
│  │  │  │  └─series
│  │  │  │  │  ├─f_0_0.txt
│  │  │  │  │  ├─f_0_1.txt
│  │  │  │  │  ├─...
│  │  │  │  │  ├─g_0_0.txt
│  │  │  │  │  ├─g_0_1.txt
│  │  │  │  │  └─...
│  │  │  └─...
│      └─session2
│  │      ├─...
  • The MSDS-ChS folder contains Chinese signatures and MSDS-TDS contains Token Digit Strings (TDS).
  • Each of them contains the data in two sessions which is stored in session1 and session2.
  • The users are arranged from 0 to 401, with online dynamic time series and offline static images provided in series and images. The time series are saved as .txt files and the images are in .png format.
  • The naming of each file follows the same format: flag_user_index.
    • flag is f or g. f indicates that this file is a skilled forgery, while g indicates that it is a genuine sample.
    • user indicates the number of user of this file.
    • index indicates the number of this file (.txt or .png) in the current folder.
    • For example, f_0_0.txt represents the first file (time series) of all skilled forgeries of the user 0. g_5_6.png represents the seventh file (image) of all genuine samples of the user 5.

📑Citation

@inproceedings{zhang2022msds,
    author = {Zhang, Peirong and Jiang, Jiajia and Liu, Yuliang and Jin, Lianwen},
    booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}},
    pages = {36507--36519},
    title = {{MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification}},
    volume = {35},
    year = {2022}
}

🌴Copyright

For commercial purpose usage, please contact Prof. Lianwen Jin: eelwjin@scut.edu.cn.

Copyright 2022-2025, Deep Learning and Vision Computing Lab, South China China University of Technology.