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How modern OCR + LLMs extract structured data from invoices, contracts, and forms — and what to watch out for.
Classic OCR returns text. Modern document AI returns structured fields — invoice number, vendor, line items, totals — typed and validated. The difference comes from layout-aware models (LayoutLM, Donut, Pix2Struct) plus a small LLM that maps extracted text to a schema.
1. Pre-process: deskew, denoise, binarize. DocFila does this on-device before anything leaves the camera.
2. OCR: extract characters, words, and bounding boxes. Apple Vision on iOS, ML Kit on Android.
3. Layout understanding: a small vision-language model groups text into headers, tables, totals.
4. Schema mapping: an LLM (or a fine-tuned classifier) maps the layout output into your target schema (invoice, receipt, ID, contract).
Hallucination on partially obscured fields — always show provenance bounding boxes and confidence scores so a human can verify.
Schema drift across vendors — keep a 'free-text fallback' so unknown fields are still captured.
Privacy — process on-device whenever possible. DocFila's free tier runs every extraction model locally on your phone.
Keep going with the document workflow that matches this page. Open the tool you need, then finish the file in DocFila.
Scan, sign, edit, translate, and store every important document — all in one beautifully simple app. No ads. No hidden fees.
Or open the web app in any browser.