What is data extraction from documents (IDP)?

OCR turns an image into text, but it doesn't know which number is the total and which is the VAT number. IDP takes the next step: it understands the structure of the document, even when every supplier uses a different format, and returns the data in tidy fields. Recent multimodal models read PDFs and photos directly, without a different model for each layout.

A good system doesn't just extract: it checks. It verifies that the sum of the lines matches the total, that the customer code exists, that the date makes sense. When it isn't sure, it sends the document to a person instead of guessing.

An example in an SMB

A building firm receives delivery notes every month from dozens of suppliers, many of them photographed on site. Lucia used to enter them by hand. Now a system reads the photos, extracts the material, quantity and site, and compares them with open orders. Lucia only sees the delivery notes with differences or with unreadable fields.

The most common mistakes

• Trusting the extraction without automatic consistency checks on the totals.
• Measuring accuracy on clean documents and then using it on crooked photos and faded scans.
• Not setting up a review queue for doubtful cases.

Why it matters to you

If someone in your admin office spends hours retyping documents, IDP is one of the AI applications with the easiest return to measure.

All the AI terms explained simply are in the glossary.