I’m unhappy with AWS Textract accuracy on low-quality scans

That’s a common pain point. Textract is strong on clean documents, but low-quality scans can really hurt OCR and table/form extraction accuracy.

A few practical ways to improve results:

1) Improve the input image first

Even small preprocessing gains can help a lot:

• Deskew rotated pages
• Increase contrast
• Denoise / sharpen lightly
• Binarize carefully for very faded text
• Crop borders and remove shadows
• Convert to 300 DPI if possible

2) Use a better scan pipeline

If you control acquisition:

• Scan at 300 DPI or higher
• Use grayscale instead of color when the source is text-only
• Avoid JPEG compression; prefer PNG/TIFF or high-quality PDF
• Ensure even lighting if using phone captures

3) Split complex documents

If documents contain mixed layouts:

• Separate pages by type
• Run OCR on problematic pages with a different workflow
• Extract tables/form regions separately if needed

4) Post-process Textract output

• Add validation rules for expected fields
• Use fuzzy matching for noisy text
• Cross-check dates, totals, IDs, and names
• Merge repeated OCR outputs from multiple preprocessing variants

5) Consider alternative OCR or a hybrid approach

For difficult scans, you may get better results with:

• Google Document AI / Vision OCR
• Azure Document Intelligence
• Open-source OCR like Tesseract with strong preprocessing
• Document layout models or custom OCR pipelines for specialized forms

6) Use a multi-pass strategy

A robust pattern is:

1. Run Textract on the original scan
2. Preprocess and run again
3. Compare confidence and outputs
4. Select the best result per page or per field

7) Train around your document types

If your documents are consistent:

• Build a custom classification/extraction pipeline
• Use templates or region-specific extraction
• Create ground truth and measure field-level accuracy

If you want, I can help you with one of these next:

• a Textract preprocessing pipeline
• a comparison of Textract vs other OCR options
• a Python example to clean scans before OCR
• a strategy for extracting fields from low-quality PDFs