OCR returns the characters on a page. Document intelligence uses that reading, then says what the document is, pulls the fields that matter, and checks them. If you searched Azure Document Intelligence, that name is Microsoft's cloud service for this work (a read model, a layout model, prebuilt models and custom models), not a different meaning of the phrase.
The two are not rivals. OCR is a step inside document intelligence. Buying a better character reader does not, by itself, tell you that a number is a lot code, or that the lot code does not match the purchase order.
Document intelligence vs OCR at a glance
| OCR | Document intelligence | |
|---|---|---|
| Output | Text | Named fields, plus what the document is |
| Document type | No opinion | Classifies before it extracts |
| Tables and forms | Flattens them into a string, unless a layout model is added | Keeps structure: rows, labels, key-value pairs |
| Meaning | Characters only | Knows which characters are the total, the date, the lot |
| Checks | None | Rules: required fields, totals, a match to another source |
| What you still build | Parsing, classification, validation, the case around it | The case around the fields, if the product stops at extraction |
What OCR returns
Optical character recognition takes an image and returns text. That job is old, and modern engines are good at it on clean print: pages become searchable, a scan becomes something you can copy, a photo of a label yields the characters on the label.
OCR stops at the string. It does not know that the string is a certificate rather than an invoice. It does not know which token is the lot and which is a phone number that happens to sit nearby. A layout-aware reader, which some products bundle with OCR and then market as OCR, keeps tables and positions. Even then, a coordinate is not a business check. Nothing in the character layer notices that the quantity on the packing list is not the quantity on the receipt.
Open-source engines and cloud read APIs are this layer. You pay for text, or for text plus layout, and you build the rest.
What document intelligence returns
Document intelligence is the layer that makes the text usable. A typical pass does five things OCR does not:
- Classification. What is this file? Certificate, packing list, invoice, ID, contract.
- Field extraction. The lot, the date, the total, the party name, taken from labels and layout, not from "the third number on the page."
- Validation. Required fields present, dates plausible, line items adding up.
- A confidence signal. Which fields the system is unsure of, so a person is not rereading the ones that are fine.
- A check against something else. The certificate against the purchase order, the packing list against the receipt. One document in isolation cannot do this. The fields have to meet the other source.
Teams also call this intelligent document processing. The label matters less than the output: structured fields you can route, or a text blob you still have to parse. AIIM publishes the industry writing on content services.
Azure Document Intelligence
Azure Document Intelligence is a Microsoft cloud service for building document processing into an application. Microsoft's overview describes document analysis models (Read for printed and handwritten text, Layout for text, tables and structure), plus prebuilt models and custom models. It is an API and a set of models. You call it, you get a result, and your application decides what to do with the result.
That is document intelligence as a component: more than raw characters, short of a finished operation. Read is the character model. Layout adds structure. Prebuilt models target common document types so you are not training from zero. Custom models cover the forms those prebuilt models do not know. The service returns a result to the application that called it. Holding a case open while a supplier sends a clearer photo, and applying your release rules, sits in the system around that API. Microsoft's own description is the Azure Document Intelligence overview.
Enterprise document OCR
Enterprise document OCR is the phrase buyers use when the volume is high, the documents are messy, and a wrong field is expensive. The requirement behind the phrase is almost never "more characters per second." It is:
- Many document types in one stream, so classification comes before extraction.
- Scans, phone photos and the occasional handwritten note, not only born-digital PDFs.
- A field-level result a downstream system can store, not a searchable PDF and a hope.
- A person on the fields the system is unsure of, with the reason, not a queue of entire packets.
- A record of what was read and what was changed.
A read API can be the first component of that. It is not the list. If the enterprise need is "put this number in this column and stop the lot when it does not match," you are buying document intelligence plus the rules, whether or not the contract says OCR.
OCR and document classification
OCR document classification sounds like one feature and is two steps. OCR reads. Classification decides the type, and the type decides which fields to look for and which rule to run.
If classification is wrong, extraction can be perfect and still useless: you pulled invoice fields off a certificate. If classification is right and extraction is a raw string, a person still maps the fields. The order is classify, then extract, then check. Products that advertise classification as part of OCR are usually selling the document intelligence layer and using the older word.
Intelligent document processing and OCR
Intelligent document processing is the product category that packages OCR, classification, extraction and a review step. OCR is the ingredient, the way a database is an ingredient in an application. Comparing "IDP versus OCR" as if they were two brands confuses a component with a product.
Horizontal IDP tools are strong at the ingredient and the review screen. They return fields. The rest of the operation is separate: ask the person who owes the missing page, apply a versioned rule, and write the outcome back to the ERP. That distinction, against one document AI product, is Floowed vs Nanonets. How agents do tasks while something else has to run the process is AI agent orchestration.
A certificate and a packing list
A goods-in clerk receives two files. One is a certificate of conformance, photographed on a desk. One is a packing list PDF.
OCR returns two blocks of text. The lot number is in there, somewhere, next to other numbers.
Document intelligence returns a document type for each file, a lot, a part, a quantity, a date, and a flag where a field is missing or low confidence. A person can check the flag instead of retyping both pages.
The case starts when those fields meet the purchase order and the warehouse receipt. The certificate's lot does not match the receipt. Someone has to ask the supplier, wait, read the new file, and decide whether the lot is released. Fields do not do that. The operation does. The same shape on a factory floor, drawings in the vault and paperwork on the dock, is manufacturing document management. An invoice that then has to match an order and a receipt is invoice approval.
Who reviews, and how much runs with no one in the loop, is a setting, not a philosophy. Human in the loop automation is that setting. Document workflow automation is what happens if you only route the file.
Where Floowed sits
Floowed is the operations platform, not an OCR engine and not an IDP. Reading documents is one step. Every case runs gather, interpret, chase, decide, act, across your systems and the people who still owe something.
Interpret is the document intelligence step: read the file in the state it arrived, including a scan or a photo, and check it against the other sources. There is no separate template per document type before a case can run. The runtime is model-agnostic and routes the reading on cost, latency and data residency. Chase is what a read API leaves to you. Decide is your rules in a deterministic policy engine. Act writes the result back.
You can keep a cloud OCR or an IDP if it already does a reading job you trust. Floowed can sit on top of the systems those tools post into. The API, MCP and integrations are on every plan. You name the operation in Slack, Microsoft Teams or the Floowed Dashboard. How it works walks the five phases without the vocabulary lesson.
Frequently asked questions
What is the difference between OCR and document intelligence?
OCR converts an image of text into characters. Document intelligence classifies the document, extracts named fields, and checks them. OCR output is a string. Document intelligence output is structured data a system can store and a rule can use.
What is Azure Document Intelligence?
Microsoft's cloud service for document processing: Read and Layout models, prebuilt models and custom models, called as an API. Read is the character layer. Layout and the prebuilt models are document intelligence as a component. The service returns a result to your application. It does not run the case around that result.
What do people mean by enterprise document OCR?
High volume, messy inputs, and a high cost when a field is wrong. The practical requirement is classification, field-level output, a review path for unsure fields, and a record of what was read. A character engine is the start of that list.
Is document classification the same as OCR?
No. OCR reads characters. Classification decides the document type, which decides which fields to extract and which rule applies. Products that sell classification under an OCR label are selling the layer above character recognition.
How do intelligent document processing and OCR relate?
Intelligent document processing is the product category that includes OCR plus classification, extraction and review. OCR is a component inside it. An IDP returns fields. Running the case those fields belong to is a further step.
Is an OCR API enough?
When you need searchable text, yes. When you need a lot number checked against a purchase order, and a person chased for the page that was missing, no. You would be building classification, validation and the case yourself on top of the text.
Is Floowed an OCR product or an IDP?
Neither. Floowed runs the operation. It reads documents as one step, including scans and photos, checks them against your other systems, chases what is missing, applies your rules and writes the result back. A read API or an IDP can remain in the stack if you already rely on one.
What does Floowed cost?
Credits, not seats. Start free with $80 of credits, no card. Team starts at $100 a month for 10,000 credits, shared by the team, not priced per user. Integrations, the API and MCP are on every plan. See pricing.
Start from a real file
Take one document your team retypes. A certificate, a packing list, an invoice photo. OCR will give you the characters. The useful question is what should happen after the fields exist, and who gets asked when they do not match.
Start free with $80 of credits and run that file through an operation. Or talk to us with the document that currently lands in a queue.