My current OCR tool misses fields on low-quality photos of receipts

That’s a common issue with receipt OCR, especially on low-quality photos. The misses are usually caused by blur, skew, glare, low contrast, tiny text, or crushed shadows.

A few ways to improve it:

• Preprocess the image

  • Deskew / rotate the receipt
  • Crop to the receipt area
  • Increase contrast
  • Denoise / sharpen lightly
  • Binarize carefully for text-heavy regions
  • Correct perspective if the photo is angled

• Use receipt-specific OCR handling

  • Detect line items, totals, tax, date, merchant separately
  • Don’t rely only on a single generic OCR pass
  • Use layout-aware parsing to recover fields from context

• Run OCR on multiple variants

  • Original image
  • Contrast-enhanced version
  • Sharpened version
  • Binarized version Then merge the best results.

• Improve capture quality

  • Encourage higher resolution
  • Better lighting
  • Less motion blur
  • Keep the whole receipt in frame

• Post-process with validation

  • Use regex and heuristics for dates, amounts, totals
  • Compare suspicious fields against nearby text
  • Cross-check total = subtotal + tax, etc.

• Consider a stronger OCR engine

  • Some engines handle receipts better than others
  • A model with document layout understanding may outperform plain OCR

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

1. a preprocessing pipeline for receipt photos,
2. field-extraction heuristics for receipts, or
3. a comparison of OCR tools/models that work better on low-quality images.