Compare · OCR / scripts
Coatables vs OCR & table-extraction scripts (pdfplumber, Tesseract)
If you have a developer and one stable lab format, scripts are a legitimate path. pdfplumber extracts tables from digital PDFs reasonably well, PyMuPDF gives coordinate-level control for merged cells, and Tesseract (or a cloud OCR API) handles scans. It’s free and fully under your control.
The weakness: brittle and unverified
The trouble starts when formats vary. A supplier changes a template and the parser silently mis-maps a column — now your “limit” column holds a method reference, and nothing errors. And even when extraction is perfect, a script only gives you the fields: it doesn’t normalize NMT/NLT, doesn’t reconcile units, and doesn’t check whether the lab’s “Pass” matches the numbers. You’ve automated the typing, not the trust.
At a glance
| OCR / table-extraction scripts | Coatables | |
|---|---|---|
| Cost | your time & upkeep | pay-per-use credits |
| Handles scans | with an OCR pass | ✓ natively |
| Survives layout/template changes | breaks, needs maintenance | ✓ vision-based |
| Normalizes limit notation & units | you build it | ✓ built-in |
| Recomputes pass/fail | you build it | ✓ |
| Cross-checks the lab’s verdict | — | ✓ |
| Flags low-confidence reads | — | ✓ |
When scripts are the right pick
One lab, one format, a developer who can maintain the parser, and no need to verify the verdict — scripts win on cost. The moment formats multiply or the pass/fail has to be right, the maintenance and the missing verification layer become the real cost.
Why Coatables
No template to maintain: a vision model reads digital PDFs and scans, then a deterministic layer normalizes limits and units, recomputes pass/fail, and cross-checks it against the lab’s printed verdict — flagging mismatches, missing limits, ambiguous units, and low-confidence scans.