# A novel segmentation dataset for signatures on bank checks

arXiv: https://arxiv.org/abs/2104.12203 · HTML: https://mlsanity.com/paper/2104.12203/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models](https://mlsanity.com/paper/2503.04021/) (2025)
- [Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification](https://mlsanity.com/paper/2406.14370/) (2024)
- [Open Annotations and Synthetic Data for Field Localisation in Indian Bank Cheques](https://mlsanity.com/paper/2606.20682/) (2026)
- [DocPure: Prompt-Free Unified Document Restoration via Degradation-Aware Structure-Guided Wavelet Modulation](https://mlsanity.com/paper/2608.09536/) (2026)

## Abstract (arXiv)

The dataset presented provides high-resolution images of real, filled out bank checks containing various complex backgrounds, and handwritten text and signatures in the respective fields, along with both pixel-level and patch-level segmentation masks for the signatures on the checks. The images of bank checks were obtained from different sources, including other publicly available check datasets, publicly available images on the internet, as well as scans and images of real checks. Using the GIMP graphics software, pixel-level segmentation masks for signatures on these checks were manually generated as binary images. An automated script was then used to generate patch-level masks. The dataset was created to train and test networks for extracting signatures from bank checks and other similar documents with very complex backgrounds.