Summary of Docxpand-25k: a Large and Diverse Benchmark Dataset For Identity Documents Analysis, by Julien Lerouge et al.
DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis
by Julien Lerouge, Guillaume Betmont, Thomas Bres, Evgeny Stepankevich, Alexis Bergès
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to identity document (ID) image analysis is proposed, addressing the challenges of automating ID verification for online services such as bank account opening or insurance subscription. The paper focuses on improving document localization, text recognition, and fraud detection methods to achieve high accuracy. However, the authors highlight the scarcity of available datasets for benchmarking ID analysis methods due to privacy concerns, security requirements, and legal restrictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Identity documents are crucial for online services like opening bank accounts or subscribing to insurance. Researchers have been working on ways to analyze these images accurately, but there’s a big problem: we don’t have many datasets to test our methods because of privacy and security issues. This paper is trying to change that by improving how we do ID image analysis. |
