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MIDV-260 is a comprehensive dataset designed specifically for the analysis, recognition, and verification of identity documents captured via mobile devices. It was created to address a specific gap in the computer vision community: while there were many datasets for standard Optical Character Recognition (OCR), there was a lack of datasets focusing on the complex, non-ideal conditions of mobile capture.

Student name: [Insert name] Course code: MIDV260 — Full Instructor: [Insert instructor] Date: April 9, 2026 midv260 full

MIDV-2020 was developed to address the scarcity of diverse, publicly available datasets for identity document (ID) recognition. It provides a high-variability benchmark for tasks such as document detection, text field recognition, and fraud prevention using mock documents with artificially generated faces and data to comply with security requirements. ResearchGate 2. Dataset Composition The dataset contains 72,409 annotated images in total, making it one of the largest in its field. Компьютерная оптика Unique Documents : 1,000 unique mock identity documents. Media Types 1,000 Video Clips

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In the rapidly evolving landscape of digital identity and computer vision, the need for robust, high-quality training data has never been more critical. As financial institutions, government bodies, and tech giants race to implement seamless "Know Your Customer" (KYC) and remote onboarding solutions, the technology must be trained to read and verify identity documents with near-perfect accuracy. Student name: [Insert name] Course code: MIDV260 —

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