Midv250 — [top]

The MIDV-250 (Mobile Identity Document Video 250) is a specialized benchmark dataset designed for the development and testing of computer vision algorithms aimed at identity document analysis and recognition in video sequences. It is a subset of the broader MIDV-500 dataset, which is a widely recognized resource in the fields of optical character recognition (OCR), document detection, and facial recognition. Overview of MIDV-250

Identity document recognition is a critical component of modern digital services, ranging from remote banking enrollment to automated airport security. However, research in this area is often hindered by the lack of publicly available datasets due to the sensitive and private nature of personal identification documents.

The MIDV-500 project, and its subset MIDV-250, addresses this gap by using "mock" documents—synthetically generated or public domain identities that mimic real-world passports, ID cards, and driver's licenses without compromising actual personal data. Key Characteristics of the Dataset

The MIDV-250 dataset is characterized by its focus on the video stream rather than static images. This allows researchers to test how algorithms perform under "in-the-wild" conditions where lighting, angles, and focus may vary frame by frame.

Document Variety: The dataset includes 250 video clips derived from a diverse range of document types, including passports, ID cards, and driving licenses from various countries. midv250

Capturing Conditions: Videos are captured using mobile devices (such as smartphones) under five distinct environmental conditions to simulate real-world usage: Table: Document lying flat on a surface.

Hand: Document held by a person, introducing slight motion blur and tilt. Partial: Occlusions where parts of the document are hidden.

Clutter: Complex backgrounds that challenge document localization algorithms.

Keyboard: Placing the document near other text-heavy objects. The MIDV-250 (Mobile Identity Document Video 250) is

Rich Annotation: Each frame in the dataset is annotated with ground truth data, including the coordinates of the document's corners (quadrangles), allowing for precise evaluation of localization and rectification models. Applications in Computer Vision Researchers use MIDV-250 to benchmark several key tasks:

A Novel Dataset for Identity Document Analysis and Fraud Detection

Here is the content breakdown for the most common references:

Quick setup and preprocessing

  1. Download images and annotation files (JSON, XML, or CSV depending on release).
  2. Organize files by document ID and capture condition.
  3. Convert annotations into your framework format:
    • For detection: convert corners to bounding boxes or polygon labels.
    • For OCR: map text fields to cropped image files + ground-truth strings.
  4. Standard preprocessing steps:
    • Resize while keeping aspect ratio (e.g., shorter side = 600 px) for detection model input.
    • Normalize pixel values per model requirements.
    • For OCR: apply brightness/contrast augmentation and deskewing to simulate mobile conditions.

MidV250: Overview, key features, and use cases

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Primary Use Cases for MIDV250

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Feature: The Phantom Update – How MidJourney v5.2 Redefined Realism

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While the tech world was fixated on the explosive launch of ChatGPT and the corporate battles of OpenAI, MidJourney quietly released an iteration in June 2023 that fundamentally shifted the baseline for AI-generated imagery. Referred to in internal logs and community discussions simply as v5.2, this version was not just a polish of its predecessor—it was a leap in aesthetic intelligence.

For digital artists and prompt engineers, the move from v5 to v5.2 (often mistyped or autocorrected in haste) was the moment the "AI look" began to dissolve.

Recognition Issues in Linux

Some early MIDV250 revisions (ID 0x125a) require the libata.force=noncq kernel parameter. Fix: Edit /etc/default/grub and add libata.force=noncq to GRUB_CMDLINE_LINUX to disable Native Command Queuing temporarily.

Example pipeline (minimal)

  1. Detect document in image using a CNN detector.
  2. Predict document corners; apply perspective warp for rectification.
  3. Crop predefined field regions from rectified image.
  4. Pass each crop to an OCR model; apply regex/validator per field.
  5. Aggregate and return structured document data.