# MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis

arXiv: https://arxiv.org/abs/2107.00396 · HTML: https://mlsanity.com/paper/2107.00396/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [From Facial Parts Responses to Face Detection: A Deep Learning Approach](https://mlsanity.com/paper/1509.06451/) (2015)
- [SSH: Single Stage Headless Face Detector](https://mlsanity.com/paper/1708.03979/) (2017)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [TinaFace: Strong but Simple Baseline for Face Detection](https://mlsanity.com/paper/2011.13183/) (2020)
- [MIDV-2019: Challenges of the modern mobile-based document OCR](https://mlsanity.com/paper/1910.04009/) (2019)
- [Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks](https://mlsanity.com/paper/1804.06039/) (2018)
- [An Automatic Reader of Identity Documents](https://mlsanity.com/paper/2006.14853/) (2020)
- [Approach for Document Detection by Contours and Contrasts](https://mlsanity.com/paper/2008.02615/) (2020)
- [Advanced Hough-based method for on-device document localization](https://mlsanity.com/paper/2106.09987/) (2021)
- [Face Detection in Camera Captured Images of Identity Documents under Challenging Conditions](https://mlsanity.com/paper/1911.03567/) (2019)
- [Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video](https://mlsanity.com/paper/2008.02566/) (2020)

## Built on by (most cited first, 30 of 32)

- [First Competition on Presentation Attack Detection on ID Card](https://mlsanity.com/paper/2409.00372/) (2024)
- [Can Multi-modal (reasoning) LLMs detect document manipulation?](https://mlsanity.com/paper/2508.11021/) (2025)
- [Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)](https://mlsanity.com/paper/2508.11716/) (2025)
- [IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection](https://mlsanity.com/paper/2408.01690/) (2024)
- [FantasyID: A dataset for detecting digital manipulations of ID-documents](https://mlsanity.com/paper/2507.20808/) (2025)
- [DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis](https://mlsanity.com/paper/2407.20662/) (2024)
- [Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries](https://mlsanity.com/paper/2409.06842/) (2024)
- [Weakly Supervised Training for Hologram Verification in Identity Documents](https://mlsanity.com/paper/2404.17253/) (2024)
- [SynID: Passport Synthetic Dataset for Presentation Attack Detection](https://mlsanity.com/paper/2505.07540/) (2025)
- [DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis](https://mlsanity.com/paper/2603.01433/) (2026)
- [Synthetic dataset of ID and Travel Document](https://mlsanity.com/paper/2401.01858/) (2024)
- [IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering](https://mlsanity.com/paper/2403.00573/) (2024)
- [FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection](https://mlsanity.com/paper/2504.07761/) (2025)
- [WebPII: Benchmarking Visual PII Detection for Computer-Use Agents](https://mlsanity.com/paper/2603.17357/) (2026)
- [Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?](https://mlsanity.com/paper/2506.05263/) (2025)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)
- [Can Generative Models Actually Forge Realistic Identity Documents?](https://mlsanity.com/paper/2601.00829/) (2025)
- [Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style](https://mlsanity.com/paper/2312.13993/) (2023)
- [From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection](https://mlsanity.com/paper/2607.01442/) (2026)
- [Identity Documents Authentication based on Forgery Detection of Guilloche Pattern](https://mlsanity.com/paper/2206.10989/) (2022)
- [Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition](https://mlsanity.com/paper/2206.13356/) (2022)
- [Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents](https://mlsanity.com/paper/2405.06701/) (2024)
- [Recurrent Few-Shot model for Document Verification](https://mlsanity.com/paper/2410.02456/) (2024)
- [ID-Card Synthetic Generation: Toward a Simulated Bona fide Dataset](https://mlsanity.com/paper/2508.13078/) (2025)
- [Exploring Light-Weight Object Recognition for Real-Time Document Detection](https://mlsanity.com/paper/2509.06246/) (2025)
- [What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection](https://mlsanity.com/paper/2606.13528/) (2026)
- [Verification of Dynamic Holographic Behavior in Identity Documents](https://mlsanity.com/paper/2607.06466/) (2026)
- [The Third Competition on Document Forgery Detection on ID-Cards and Passports](https://mlsanity.com/paper/2607.15734/) (2026)
- [FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking](https://mlsanity.com/paper/2607.26641/) (2026)
- [Towards Zero-Shot Domain Generalization for ID Cards Presentation Attack Detection](https://mlsanity.com/paper/2608.16591/) (2026)

## Abstract (arXiv)

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In addition, the published datasets were typically designed only for a subset of document recognition problems, not for a complex identity document analysis. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. For the presented benchmark dataset baselines are provided for such tasks as document location and identification, text fields recognition, and face detection. With 72409 annotated images in total, to the date of publication the proposed dataset is the largest publicly available identity documents dataset with variable artificially generated data, and we believe that it will prove invaluable for advancement of the field of document analysis and recognition. The dataset is available for download at ftp://smartengines.com/midv-2020 and http://l3i-share.univ-lr.fr .