Midv-112 | FHD · 1080p |

In terms of technical composition, the dataset is divided into training and testing sets to ensure unbiased evaluation. It includes images with different backgrounds—ranging from neutral office settings to cluttered domestic environments—to simulate the unpredictability of mobile capture. The inclusion of documents with complex security backgrounds and transparent elements further pushes the boundaries of current recognition technology.

The document MIDV-112, also known as the MIDV-2020 dataset, is a specialized collection of document images designed to advance the field of automatic identity document analysis. Created by researchers at the Smart Engines Service and the Russian Academy of Sciences, it serves as a critical benchmark for developing algorithms that can recognize and process identity documents in diverse, real-world conditions. midv-112

For the industry, MIDV-112 facilitates the creation of more reliable remote identity verification (eKYC) solutions. As more services—from banking to car sharing—move toward digital onboarding, the ability to accurately verify a user's ID via a smartphone becomes paramount. Tools trained on datasets like MIDV-112 help reduce friction for users while maintaining high security standards against fraud and document tampering. In terms of technical composition, the dataset is

MIDV-112, featuring Yua Mikami, is a 2023 MOODYZ studio production representing a significant high-production idol-themed release towards the end of the actress's career. Released on May 2, 2023, the 120-minute film blends cinematic storytelling with the "S-Class" idol persona, falling within the "Miss Idol" series. The document MIDV-112, also known as the MIDV-2020

The impact of MIDV-112 on the research community has been significant. It has become a standard reference in academic papers focusing on computer vision and document image analysis. By providing a common ground for comparison, it enables researchers to measure the progress of new architectures, such as deep convolutional neural networks and transformers, in the specific context of identity document processing.

The dataset contains 112 unique document types, which gives the collection its name. These include a wide array of international identity cards, passports, and driving licenses from various countries. For each document type, the dataset provides video clips and individual frames captured on different mobile devices. This variety ensures that the algorithms developed using MIDV-112 can handle different layout structures, fonts, and security features common in global identity documents.

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