Midv-578

The MIDV-578 dataset is a cornerstone for several critical technologies in the fintech and security sectors:

The original collection featuring 500 video clips of 50 different identity document types. It focused on the basic challenges of mobile capture, such as perspective distortion and varying lighting. MIDV-578

Banks and digital services use models trained on MIDV-578 to verify identities via smartphone cameras, ensuring that the system can read a driver's license from a remote region just as easily as a local passport. The MIDV-578 dataset is a cornerstone for several

An expansion that introduced more complex backgrounds and higher-resolution captures. An expansion that introduced more complex backgrounds and

The dataset is engineered to simulate the "noise" of real-world mobile interactions. Key technical characteristics include:

Developed as part of the broader series by researchers at the Institute for Information Transmission Problems and Moscow Institute of Physics and Technology, this dataset addresses the growing need for robust AI models capable of processing identity documents in uncontrolled, real-world environments. The Evolution of the MIDV Datasets

Before reading text, a system must "find" the document in a video frame. MIDV-578 provides the ground truth (exact coordinates) needed to train these detection models.