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OCR vs LLM: how document data extraction really works
3 min read
Anyone evaluating document automation runs into two acronyms straight away: OCR and LLM. They are often presented as alternatives. In practice they do two different jobs, and the best results come when they work together.
What OCR does, and where it stops
OCR (Optical Character Recognition) turns an image into text: it takes the scan of an invoice or a non-selectable PDF and returns the characters it contains. It is a mature, fast and cheap technology.
The limit is that OCR recognises characters without understanding what they mean. It knows that a certain position reads "240"; it does not know whether that is a quantity, a price or a line number. That is why classic OCR systems have to be configured for each layout: one template for supplier A's invoices, another for supplier B's, and when a supplier changes its template the extraction breaks.
What a Large Language Model adds
An LLM interprets the content. Given the text of a document, it can tell which number is the quantity and which is the price even when the document changes shape, the columns are in a different order or the information sits in a sentence rather than a table.
This means a single flow can handle heterogeneous documents without a template for each: quote requests typed freely into an email, orders as PDFs, packing lists in Excel. Messy formats become the manageable norm instead of the exception someone handles by hand.
The confidence score
The real operational leap is that every extracted field can carry a confidence score. "Customer: Acme Corp, 98%" and "Deadline: 15 July, 72%" are not the same thing: the first can move on by itself, the second should be shown to a person.
With this mechanism the automation does not have to be perfect to be useful. Safe cases pass, doubtful cases are routed to human review, and every decision stays on record.
Why they are used together in practice
An LLM works on text. If the document is a scan, something has to turn pixels into characters first, and that something is OCR. The typical flow is therefore OCR to read, LLM to understand, confidence score to decide who takes the next step.
For documents that are already digital (native PDFs, emails, web forms) the OCR step is unnecessary and the LLM works directly on the content.
When classic OCR is enough
If you receive a single type of document, always from the same system and with the same layout, template-based OCR may be sufficient and cheaper. The value of an LLM shows when documents vary, arrive from many sources or the useful information is written in natural language.
The quickest way to find out is to look at a sample of your real documents together: it is the first step of every project we run.