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MIDV-720 (2021) — Complete Guide

Overview

MIDV-720 is a publicly released dataset (2021) for identity document analysis and recognition, containing 720 high-resolution images of 9 identity document types with variations in lighting, orientation, and background. It's widely used for training and evaluating OCR, document detection, layout analysis, and face/photograph localization models.

Document detection pipeline:

Access Method: You must sign a usage agreement with the dataset maintainers (usually via the University of Fribourg or the Russian Academy of Sciences, depending on the distribution mirror).
Popular Platforms: While not on standard open data sites like Kaggle (due to size and licensing), it is frequently cited on PapersWithCode and can be found via academic institutional access through Hugging Face datasets (sanitized versions).

Example Research Workflow Using MIDV-720

Preprocess: apply color normalization, resize, and augment (rotation, brightness, occlusion).
Detect: use an object detector (e.g., YOLO, Faster R-CNN) to find document contour or corners.
Rectify: estimate homography and warp to frontal view.
OCR: apply a text recognition model (e.g., CRNN, transformer-based recognizer) to extracted fields.
Evaluate: report IoU for detection, homography corner error, and CER/WER for OCR.

Based on the search results, there is no direct evidence or "report" publicly available for a specific entity or product named "MIDV-720" from 2021. midv720 2021

Correction: MIDV-720 was part of the “extreme pleasure” / "trembling orgasm" series featuring Miru (Sakamichi Miru). Known for her athleticism and intense reactions, Miru is the sole focus. MIDV-720 (2021) — Complete Guide Overview MIDV-720 is

Based on the identifier "midv720 2021", you are referring to a dataset and benchmark paper widely used in the field of Computer Vision and Artificial Intelligence. Access Method: You must sign a usage agreement

Comparing MIDV720 2021 to Other Datasets

1. Liveness Detection

The most common use case. By studying the "Presentation Attack" videos (replays/prints), AI models learn to distinguish a real plastic ID from a screen or paper fake. The 2021 dataset is unique because it includes moiré patterns—the wavy lines that appear when filming a screen—which are a dead giveaway of a replay attack.