OCR and document parsing service with confidence scores and API

Every day we process a stream of scanned documents and we're tired of retyping them by hand. We need a service that takes an uploaded image or multipage scan and returns clean structured data: not just a wall of recognized text, but exactly the fields that matter to us. For an invoice this is vendor, invoice number, date, line items, tax, and total. For a receipt this is merchant, date, amount, and currency. For an ID this is name, document number, and expiration date. Each extracted value must have a confidence score so the operator immediately sees which fields to trust and which to double-check. The task is specifically about reading and extracting data from documents, so don't offer us PDF generation or media libraries—that's a different project.

The result has two parts that must work together. First, a reliable extraction pipeline: image cleanup and alignment, OCR across different print qualities and multiple languages, then a parsing layer that maps raw text to the named fields above. Second, a simple verification interface where a human opens a document, sees recognized fields overlaid on the original scan, and corrects anything the model got wrong before approval. Low-confidence fields should be highlighted so verification is fast. All of this must also be available programmatically: a REST endpoint that takes a file and returns JSON with fields, values, bounding box coordinates, and confidence, plus webhooks or status endpoints for async batches.

We're flexible on technology: cloud vision models, open source OCR stacks, or a hybrid will work as long as accuracy is high and cost per page is predictable. Share examples of similar extraction tasks and honestly assess accuracy on real, imperfect scans.

— Extract structured fields from invoices, receipts, and IDs
— Confidence score for each field with highlighting of unreliable ones
— Verification interface with field overlay on original scan
— REST API returning JSON: fields, values, coordinates, confidence
— Batch processing with async status or webhooks
— Predictable price per page and documented accuracy on scans