PDFs, OCR, and document AI

Overview

• Document AI combines text extraction, OCR, layout/table understanding, and field extraction.
• A PDF may already contain usable text or require image interpretation.
• OCR reads characters; document understanding connects them to fields and structure.

Why this matters

Business documents contain tables, stamps, signatures, and inconsistent layouts that plain text parsing can misinterpret.

An everyday example

A clerk reads a bill and understands which number is the tax, total, or invoice ID.

A technical example

Extract invoice number, vendor, date, and item totals, then check sum(items) + tax = total in code.

How it works

1. Validate file
2. Detect text/scanned pages
3. Parse/OCR
4. Extract structured fields with locations
5. Validate exact values
6. Review ambiguous fields
7. Publish approved data.

Architecture and flow

Document → parse/OCR/layout → field extraction → arithmetic/domain checks → review/store.

The insurance assistant example

Populate a claim draft from repair invoices and police reports while preserving page/box evidence for staff.

When to use it

Repeated semi-structured documents and paper-like inputs.

When to avoid it

Prefer a reliable structured supplier API over re-reading its exported document.

Interview answer

“Document AI extracts fields and structure; I validate totals, dates, identifiers, and evidence locations before those values enter business operations.”

Benefits, trade-offs, and production considerations

• Extraction confidence requires calibration.
• Use field-level review rather than assuming every field in one document shares the same certainty.
• Sandboxed parsers and resource limits protect against malicious files.