Midv075 -

Title: Robust Identity Document Recognition in Uncontrolled Video Streams: A Study of the MIDV Benchmark Framework Abstract

Automated recognition of identity documents is critical for remote KYC (Know Your Customer) and AML (Anti-Money Laundering) processes. However, challenges such as variable lighting, complex backgrounds, and motion blur often degrade performance. This paper explores the MIDV (Mobile Identity Document Video) dataset family, specifically MIDV-2020, as a primary benchmark for developing robust computer vision models. We discuss the dataset structure, baseline recognition tasks—including document localization, face detection, and Optical Character Recognition (OCR)—and the implications for real-world document forensics. 1. Introduction

Traditional OCR systems often fail when processing photos captured on mobile devices due to "uncontrolled environments." These environments include:

Variable Lighting: Glares from lamination or shadows from the device.

Geometric Distortion: Perspective shifts when the document is not parallel to the camera.

Background Clutter: Documents placed on textured surfaces like keyboards or held in hands.

The MIDV-500 Dataset and its successor, MIDV-2020, were created by researchers at Smart Engines to address these issues by providing thousands of annotated video frames of mock IDs. 2. Dataset Composition

The MIDV family utilizes "mock" documents to ensure data privacy while maintaining high realism.

MIDV-500: 50 document types (passports, ID cards, licenses) with 500 video clips. midv075

MIDV-2020: 1,000 unique mock documents with artificially generated faces and text, totaling over 72,000 annotated images.

Annotation Quality: Each frame includes ground truth for document boundaries (quadrangles), text field locations, and face ovals. 3. Technical Challenges & Benchmarks The MIDV-2020 benchmark evaluates three core tasks:

Document Localization: Finding the four corners of the ID against complex backgrounds using semantic segmentation or feature-based methods like SURF or BEBLID.

Face Detection: Locating the portrait on the document to verify the holder's identity.

Field Recognition (OCR): Extracting text from specific zones (e.g., Name, DOB, Document Number) using systems like Tesseract. 4. Recent Advances: Fraud & Forensics

Beyond simple recognition, newer iterations like MIDV-Holo and MIDV-DynAttack introduce:

Hologram Detection: Authenticating security features that change appearance with camera movement.

Presentation Attack Detection (PAD): Identifying if the document is a physical card, a screen capture, or a high-quality photocopy. 5. Conclusion MIDV-075 is a publicly released dataset from the

The MIDV framework continues to be the foundation for modern identity verification research. By providing a diverse range of scripts (including Urdu and Persian in MIDV-UP) and capture conditions, it enables the development of AI that can handle the unpredictability of mobile-first identity verification.

Do you need the Python code for document localization using this dataset?

Should I focus on a specific fraud detection technique (like hologram analysis)?

MIDV-075 is a publicly released dataset from the MIDV (Mobile Identity Document Video) series used for research on identity-document analysis (detection, recognition, OCR, and document attribute extraction). MIDV-075 contains images and video frames of identity documents captured in varied realistic conditions to support development and evaluation of computer-vision and OCR algorithms.

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