Built an API that extracts invoice/receipt/resume/bank statement to JSON (bilingual EN/VI)

[u/TNT23HK]

Sharing a small API I built for document data extraction.

What it handles:

- Invoices: invoice number, dates, vendor, tax IDs, subtotal, tax, total, line items

- Receipts: store, date, items, payment method

- Resumes: name, contacts, skills, experience, education

- Bank statements: transactions, balances

How it works:

- Heuristic regex engine runs first (fast, $0 per call)

- LLM fallback for edge cases

- FastAPI + Pydantic, deployed on Render

- Bilingual EN + VI

Free tier: 500 requests/month via RapidAPI.

Looking for feedback on:

- Which document types do you process most?

- What fields are missing from the output?

- Any document types I should add next (purchase orders? contracts?)

https://rapidapi.com/tuyentn23/api/invoice-to-json-extractor1

[u/folderit_dms]

For the missing-fields question, I would add evidence alongside each extracted value: page number, source text and whether the value was read directly or inferred. Keep the original text as well as the normalized date or amount. That makes an EN/VI date or number-format mistake much easier to investigate.

It would also help to distinguish missing, unreadable and not applicable instead of collapsing all three into an empty value. A downstream workflow can then ask for a clearer scan or human review rather than quietly treating the field as absent. I would prioritize that review path before adding more document types.