What's the most reliable text detection model for digitizing scanned forms with messy layouts and low-quality images?

For scanned forms with messy layouts and low-quality images, the most reliable choice is usually a document AI pipeline, not just a plain text detector.

Best practical answer

• Google Document AI / Vision OCR: very strong for degraded scans and complex forms; good layout handling.
• Microsoft Azure Document Intelligence (Form Recognizer): also excellent for forms and structured extraction.
• ABBYY FineReader / ABBYY Vantage: often one of the best for noisy scans, especially in enterprise OCR workflows.

If you specifically mean an open-source text detection model

A strong option is:

• PaddleOCR (PP-OCR + DB/DB++ text detector)
  Reliable, fast, and widely used for messy real-world documents. It handles uneven layouts reasonably well and is usually a better starting point than older detectors like CRAFT for production use.

Recommended setup for your case

For low-quality scanned forms, the best results usually come from:

1. Image pre-processing
   • deskew
   • denoise
   • contrast enhancement
   • binarization if needed
2. Text detection
3. Text recognition
4. Layout/form understanding
   • table detection
   • key-value extraction
   • line item grouping

Quick recommendation by use case

• Highest reliability overall: ABBYY or Google Document AI
• Best open-source choice: PaddleOCR
• If layout is extremely messy: use a full document understanding model/pipeline, not detector-only

If you want, I can also recommend the best model specifically for open-source, best cloud API, or best full pipeline depending on your budget and deployment needs.