Midv-682 -

MIDV-682

MIDV-682 is a benchmark dataset and evaluation task used in the field of document analysis and optical character recognition (OCR), specifically for robust text detection and recognition from images of identity documents captured under unconstrained conditions (smartphone photos, varied lighting, rotations, occlusions). It extends earlier MIDV datasets that focus on printed identity documents and supports research in fields like identity verification, automated document parsing, and mobile OCR.

Common methods and models used

• Document detection: Faster R-CNN, YOLO-family, Mask R-CNN, DETR variants, specialized document detectors.
• Perspective correction: Homography estimation via keypoint matching, Transformer-based regression, or deep homography networks.
• Text detection: EAST, CRAFT, DB-Net, text segmentation models.
• Text recognition: CRNN, Transformer OCR models (e.g., TrOCR-like), attention-based sequence-to-sequence recognizers.
• End-to-end systems: Pipelines combining detection + rectification + OCR, or unified models performing joint detection and recognition.

1. Title Information

What is MIDV-682?

It looks like you’re referring to a feature tracked under the identifier MIDV‑682. To help you effectively, I’ll need a bit more context. Could you let me know: MIDV-682

4. Scope & Boundaries

| In Scope | Out of Scope | |----------|--------------| | • Automatic tag generation for image (JPEG, PNG, GIF) and video (MP4, WebM) files
• Client‑side inference (no server‑side AI calls)
• UI integration in the existing “Upload → Edit” flow
• Ability to customize the taxonomy via admin settings | • Full‑text description generation (captions)
• Audio‑only assets
• Integration with external AI providers (e.g., AWS Rekognition)
• Bulk‑edit operations on existing assets (to be covered in a later ticket) | MIDV-682 MIDV-682 is a benchmark dataset and evaluation