Intelligent Document Processing — AI Glossary

Reference details

Related terms

• OCR (Optical Character Recognition)
• Information Extraction
• Document Q&A

How it works

1. Ingestion normalises heterogeneous sources into a working representation, since a scan, a native PDF and a spreadsheet need different handling.
2. Layout analysis recovers reading order and structure — columns, headers, tables — which is the step that distinguishes this from plain text extraction.
3. Classification routes a document to the right extraction schema, because an invoice and a contract need different fields.
4. Extraction pulls named fields against that schema, so the output is structured data rather than prose.
5. Validation checks the extracted values against rules and confidence thresholds, routing low-confidence cases to human review rather than passing them downstream.

Example: Why per-field confidence beats a document score

A pipeline extracts 12 fields per invoice at 97% accuracy each. If errors were independent, the chance of a fully correct document is 0.97¹² ≈ 0.69 — so about 31% of documents contain at least one wrong field despite every field looking reliable. Routing on a document-level score sends 31% to review. Routing per field sends only the low-confidence fields, perhaps 3% of all fields, which is a far smaller review queue for the same correctness.

Commonly confused with

Used interchangeably with OCR, which is one step inside it. OCR converts images of text into characters; this is the whole pipeline from ingestion through layout, classification, extraction and validation, and most of its failures are not OCR failures. A native PDF needs no OCR at all and can still fail on layout. The distinction matters when diagnosing: if a table came out scrambled on a digital PDF, the character recognition was never involved.

When to use it

Reach for it when:

• Structured fields must be extracted from documents at volume with a review path.
• Sources are heterogeneous enough that one extraction route cannot serve them.
• Downstream systems need validated data rather than text.

Reach for something else when:

• Documents are already structured data, where parsing suffices.
• The output is prose for a human to read, where extraction adds a schema nobody needs.
• No review path exists for low-confidence extractions, which pushes errors silently downstream.

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