I used Gemini 2.5 Flash to parse receipts at scale. Here’s what I learned about multimodal OCR in production
artificial
I used Gemini 2.5 Flash to parse receipts at scale. Here’s what I learned about multimodal OCR in production
For my startup, I needed to extract structured data (item name, price, quantity, unit cost) from photos of receipts and from product images on the shelf; faded thermal paper, crumpled, bad lighting, the works. Key findings after thousands of test...
artificial

I used Gemini 2.5 Flash to parse receipts at scale. Here’s what I learned about multimodal OCR in production

I used Gemini 2.5 Flash to parse receipts at scale. Here's what I learned about multimodal OCR in production

For my startup, I needed to extract structured data (item name, price, quantity, unit cost) from photos of receipts and from product images on the shelf; faded thermal paper, crumpled, bad lighting, the works.

Key findings after thousands of test receipts:

  • Single-pass extraction beats two-step pipelines. Most setups use a vision model for OCR then a language model for structuring. Gemini does both in one call, faster and cheaper.
  • Prompt structure matters more than model size. Asking for JSON with strict field definitions dramatically outperformed open-ended extraction prompts.
  • Thermal fade is the hardest edge case. The model handles blur and angle well. Faded thermal paper causes the most hallucinations, still working on mitigation strategies.
  • Flash vs Pro tradeoff: Flash handles ~95% of receipts correctly. Pro kicks in for complex layouts (multi-column, handwritten addendums). The cost difference makes routing worth it.

Happy to share more specifics on prompt design if anyone's working on similar problems.

submitted by /u/AdEfficient8374
[link] [comments]