Human-in-the-loop (HITL) in document processing means routing uncertain extractions to a human reviewer before they go downstream. The system extracts what it can confidently. Anything it’s uncertain about goes into a review queue. A person resolves it. The validated result continues.
Confidence scoring is a mechanism that assigns a reliability score to each field extracted from a document. Instead of returning a value and treating it as correct, the system also returns a number that represents how certain it is that the extraction is right.
Every document automation project starts the same way. You pick a tool, write some code, test it on a handful of documents — and it works. Fields are extracted, outputs look right. You ship it.
Then the edge cases arrive.
Intelligent Document Processing (IDP) is a category of software that extracts structured data from unstructured documents — automatically and reliably, even at high volume.
The document arrives as a PDF, an image, or a scan. IDP reads it, identifies what matters, and outputs structured data: fields, values, tables — in the format your system expects. No manual entry. No copy-paste.
Azure Document Intelligence (formerly Form Recognizer) is Microsoft’s managed IDP service. It handles invoices, receipts, purchase orders, and ID documents well — out of the box, with no custom training required for standard formats.
For many use cases, it’s a reasonable starting point. For many production workflows, it’s not enough.