: The largest publicly available dataset, containing over 72,000 annotated images with unique synthetic faces and text fields to protect privacy while maintaining realism. What "Verified" Means in This Context
To overcome this, the computer vision community introduced the MIDV family:
The MIDV datasets were created by researchers at Smart Engines (Moscow) in collaboration with several European universities. Because real identity documents are protected by privacy and security regulations, public‑domain datasets are extremely scarce. The MIDV family solved this problem by generating that contain no real personal data, while still preserving the visual appearance, text fields, and security features of real IDs. midv250 verified
If you are looking for the technical documentation or the dataset files themselves, they are frequently hosted on platforms like GitHub or Kaggle .
The MIDV series, developed by researchers at , is the global standard for training and benchmarking mobile ID recognition systems. : The largest publicly available dataset, containing over
As remote identity verification becomes increasingly common in digital services, the importance of open, high‑quality datasets like MIDV‑2020 will only grow. Understanding the capabilities and limitations of these benchmarks is the first step toward building more robust, trustworthy, and verifiable identity systems for the future.
When a user or a document is labeled as it means they have passed a rigorous screening process that meets global security standards, such as KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations. How the Verification Process Works The MIDV family solved this problem by generating
Before extracting data, an AI must know exactly what it is looking at. Is it an ID card from Spain, a passport from Latvia, or a driver's license from Finland? A verified pipeline successfully matches the geometry and feature maps of the captured image against a predefined document database layout.