OCR turns an image of text into text. Intelligent document processing, or IDP, goes further: it classifies a document, extracts the fields a business cares about, checks them and passes them on. One reads characters; the other produces data a system can act on.
The two are not rivals. Most IDP systems use OCR as a step, so the useful question is what you need after the text exists.
What OCR does
Optical character recognition converts images of typed, handwritten or printed text into machine-encoded text, as Wikipedia defines it. The idea is old: an optophone for reading aloud appeared in 1914, and omni-font OCR was unveiled in 1976. Today the open-source engine Tesseract reads more than 100 languages and returns plain text or formats such as hOCR and PDF, and cloud services such as Azure and AWS Textract read printed and handwritten text.
What comes out is text. It carries no document type, no typed values, no check that an amount is right and no business rule. IBM notes that OCR can be error-prone, so extracted data may need manual review.
What intelligent document processing adds
IBM describes IDP as using AI and machine learning to classify documents, extract information and validate data. ABBYY lists input, OCR, classification, extraction and validation as stages, and Google Document AI offers separate processors to read text, parse forms and tables, extract custom entities, classify and split documents.
The table puts the two side by side on the dimensions that matter once the text exists.
| OCR alone | IDP | |
|---|---|---|
| Output | Text, sometimes with layout and positions | Typed fields against a schema, plus a document type |
| Tables and forms | Lines and words | Table structure and key-value pairs |
| Fields | None | Prebuilt or custom schemas |
| Document type | Not identified | Classified, and multi-document files split |
| Validation | None | Checks against rules and other fields |
| Exceptions | A person reads the text | Low-confidence or failing fields routed to review |
| Integration | Text to be processed further | Structured data ready for a system |
Two caveats keep the table honest. Modern OCR engines read handwriting, although accuracy varies with the writing, so handwriting is not what separates the two. And human review is a common practice in IDP, not a stage every vendor lists.
The same invoice, two ways
The example is illustrative and uses fictional data. OCR returns the text of the page, in reading order:
ACME HOSTING S.L.
Invoice INV-2041 14 March 2026
Hosting 1 400.00
Support 2 300.00
Subtotal 700.00
VAT 21% 147.00
Total 847.00An IDP step takes that text and returns what a system needs:
{
"document_type": "invoice",
"invoice_number": "INV-2041",
"issue_date": "2026-03-14",
"supplier": "Acme Hosting S.L.",
"total": 847.0,
"checks": { "lines_plus_tax_equal_total": true }
}The second output is shorter and more useful, because it is typed, named and checked: 700 plus 147 equals 847. If the check failed, the document would go to a person instead of into the ledger.
Where large language models fit
Older IDP relied on templates and trained models, which work well on documents with common layouts. Microsoft says template-trained models suit structured documents with common templates, while language-model approaches handle varying layouts without labelled training data. ABBYY describes a hybrid: rules for structured documents, machine learning for semi-structured ones and language models for unstructured content, with the outputs validated by the structured layers around them.
None of these sources says a language model replaces OCR. Reading the characters is still a step; what changes is how fields are found and how much setup a new document type needs.
When OCR alone is enough
OCR on its own is enough when the goal is searchable or copyable text, for example to build a search index, to archive scanned pages or to read fixed-position forms, and when a person reads the result afterwards. It is not enough when a system must act on typed values, tell document types apart, check values or route exceptions. Any of those needs the steps above.
Where anyformat fits
anyformat is a document extraction platform built around that second list. A workflow parses the document, can classify and split it, extracts the fields you define in a schema and validates them, and each field comes back with a confidence score and the source text and page it was read from, which is what lets low-confidence fields go to a person. OCR is one of the steps inside parsing.
We will not call it the best for every case, because that depends on your documents and no benchmark covers every tool. What it is built to do differently comes down to five things. You define the fields you want in a schema and it works on layouts it has never seen, with no template per supplier. Every extracted field carries a confidence score and the source text and page it was read from, so a reviewer checks the uncertain fields instead of rereading the whole document. Validation rules sit in the same workflow, and fields that fall below a threshold can be routed to a person, instead of being a system you build around an OCR engine. It is EU-headquartered and ISO 27001 certified, and it can run on-premise, including air-gapped, when documents cannot leave your perimeter. And the same workflow can be used from code, from the no-code Studio or from an agent through MCP.
For the metrics that matter once documents reach production, see beyond accuracy, and for OCR on Spanish documents see the best OCR APIs for Spanish documents.
Frequently asked questions
What is the difference between IDP and OCR?
OCR converts an image of text into text. IDP classifies the document, extracts typed fields, validates them and passes them to a system. OCR is usually one step inside IDP.
Does IDP use OCR?
Usually yes. ABBYY lists OCR as a stage of IDP, and cloud services such as Azure and Google offer OCR and field extraction as parts of one offering.
Can OCR extract specific fields like an invoice total?
OCR returns the text, including the total, but it does not know which number is the total. Identifying it needs extraction rules or a model, which is the IDP step.
Is OCR still needed with large language models?
Reading characters from images is still a step in many pipelines. Language models change how fields are found and validated, and how much setup a new document type needs.
When is OCR enough?
When you need searchable or copyable text, archives, or fixed-position forms, and a person reads the result. Beyond that, you need extraction and validation.
