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

06 Jun 2022, 13:48 (modified: 16 Oct 2022, 11:55)NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Handwriting Verification, Datasets, Handwritten Token Digit String, Handwritten Chinese Signature, Multimodal
Abstract: 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.
Supplementary Material: pdf
Dataset Url: Our dataset is publicly available at https://github.com/HCIILAB/MSDS.
License: Our dataset is released under the Creative Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.
Author Statement: Yes
Contribution Process Agreement: Yes
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