# DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis

arXiv: https://arxiv.org/abs/2407.20662 · HTML: https://mlsanity.com/paper/2407.20662/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 30 Jul 2024.

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

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)
- [MIDV-2019: Challenges of the modern mobile-based document OCR](https://mlsanity.com/paper/1910.04009/) (2019)
- [An Automatic Reader of Identity Documents](https://mlsanity.com/paper/2006.14853/) (2020)
- [Advanced Hough-based method for on-device document localization](https://mlsanity.com/paper/2106.09987/) (2021)
- [LDRNet: Enabling Real-time Document Localization on Mobile Devices](https://mlsanity.com/paper/2206.02136/) (2022)

## Built on by (most cited first, 5 of 5)

- [WebPII: Benchmarking Visual PII Detection for Computer-Use Agents](https://mlsanity.com/paper/2603.17357/) (2026)
- [Can Generative Models Actually Forge Realistic Identity Documents?](https://mlsanity.com/paper/2601.00829/) (2025)
- [Verification of Dynamic Holographic Behavior in Identity Documents](https://mlsanity.com/paper/2607.06466/) (2026)
- [Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs](https://mlsanity.com/paper/2608.12911/) (2026)
- [IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]](https://mlsanity.com/paper/2609.03052/) (2026)

## Abstract (arXiv)

Identity document (ID) image analysis has become essential for many online services, like bank account opening or insurance subscription. In recent years, much research has been conducted on subjects like document localization, text recognition and fraud detection, to achieve a level of accuracy reliable enough to automatize identity verification. However, there are only a few available datasets to benchmark ID analysis methods, mainly because of privacy restrictions, security requirements and legal reasons. In this paper, we present the DocXPand-25k dataset, which consists of 24,994 richly labeled IDs images, generated using custom-made vectorial templates representing nine fictitious ID designs, including four identity cards, two residence permits and three passports designs. These synthetic IDs feature artificially generated personal information (names, dates, identifiers, faces, barcodes, ...), and present a rich diversity in the visual layouts and textual contents. We collected about 5.8k diverse backgrounds coming from real-world photos, scans and screenshots of IDs to guarantee the variety of the backgrounds. The software we wrote to generate these images has been published (https://github.com/QuickSign/docxpand/) under the terms of the MIT license, and our dataset has been published (https://github.com/QuickSign/docxpand/releases/tag/v1.0.0) under the terms of the CC-BY-NC-SA 4.0 License.