IDP vs OCR: the difference and when OCR is not enough

OCR and IDP both turn a document into data, and the two terms often get used as if they mean the same thing. They do not. OCR reads characters: it converts the marks on a page into machine-readable text. IDP understands documents: it works out what a document is, finds the fields that matter, checks them against your records, and gets better as people correct it. If every invoice you received arrived in one fixed layout, OCR on its own might be enough. Real supplier invoices do not behave that way, and that is where the difference starts to cost a finance team time.

What OCR does

Optical character recognition is the older of the two technologies, and it does one job well. Point it at a scanned page, a photographed receipt or a PDF and it converts the shapes of the letters and numbers into text a computer can store, search and copy. The document stops being a flat image and becomes a string of characters. That is genuinely useful. It is how a warehouse of paper archives becomes searchable, and how a photographed receipt turns into something you can paste into a spreadsheet.

The ceiling shows up the moment you ask OCR to do more than read. Reading a page is not the same as understanding it, and an accounts payable team needs the second thing. These are the limits that matter in practice:

• Layout variance. OCR reads the marks where it finds them. Every supplier formats an invoice differently, so without a template telling it where the total, the VAT and the invoice number sit, OCR returns a wall of text with no idea which number is which.

• Templates. To get structured fields out of plain OCR you build a template, or zone map, for each layout. That is workable for a handful of regular senders. Across hundreds of suppliers who each redesign their invoices from time to time, maintaining those templates becomes a job in itself.

• Handwriting and annotations. A handwritten note, a signature, a stamped approval or a scribbled purchase order number sits outside what character recognition was built for, and these are common on real documents.

• Context. OCR has no concept of what a field means. It can read the string 1,240.00 perfectly and still cannot tell you whether that figure is the net, the VAT or the gross, because meaning is not something character recognition holds.

None of this makes OCR a bad tool. It makes it a reading tool asked to do an understanding job. On an accounts payable desk that gap shows up as an operator who still opens every document, reads the fields OCR could not label, and keys them into the ERP by hand. The characters were recognised. The work of turning them into a posted invoice was left exactly where it started.

What IDP adds

Intelligent document processing keeps OCR as one step and builds the understanding around it. It uses computer vision and language models to read the page as a structured document, working out what each part of it means. Four things sit on top of the reading:

• Classification. IDP first works out what each document is, an invoice, a credit note, a purchase order, a statement or a delivery note, before it reads a single field. That matters because the right extraction logic depends on knowing what you are looking at.

• Extraction with context. It reads fields by what they mean. It knows the supplier name, the invoice number, the net, the VAT, the gross and the line items whatever layout they arrive in, so a supplier you have never seen before can still come through as labelled data, usually with no template to build. How well that works on unusual layouts varies by product, which is why testing on your own invoices matters.

• Validation. The extracted data is checked before anyone trusts it. Does the purchase order exist, do the line totals add up to the invoice total, is the VAT rate plausible, is the supplier on file. Anything that fails is held as an exception with the problem shown, so bad data surfaces for review before it reaches the ledger.

• Learning from corrections. When a person fixes a field the model uses that correction, so the same supplier reads correctly next time. In systems built this way, accuracy climbs as the system sees more of your documents, which a fixed template does not do on its own.

How they compare, side by side

The clearest way to see the difference is to run the same invoice through both. OCR hands back the characters on the page. IDP hands back named fields, each one checked against your records:

COBALT PARK ENGINEERING LTD
Unit 4 Riverside Way Sheffield
INVOICE INV-2025-04587 13/04/2025
Your ref PO-8834
Structural steel fabrication 1 6,200.00 6,200.00
Site delivery & crane hire 1 1,450.00 1,450.00
Net 8,450.00 VAT 20% 1,690.00
TOTAL DUE 10,140.00

Set the two against each other on the dimensions a finance team actually feels:

The pattern across every row is the same. OCR gives you a faithful copy of what is on the page and stops there. IDP carries the document the rest of the way, to labelled, validated data that a downstream system can act on without a person in the middle. For a finance team that downstream system is the ERP, and the distance between a page of recognised text and a posted invoice is the distance IDP is built to cover.

When OCR is fine

OCR is a narrow technology, and there are jobs where it is exactly the right tool. When the work is purely reading, OCR alone does the job without the extra machinery.

It fits when you are digitising archives so old paper becomes searchable, when you need plain text out of a document for storage or search, or when you have a single stable layout that never changes and a template will hold. A mailroom turning scanned correspondence into searchable PDFs, or one supplier who sends the same fixed invoice design every month, is well served by OCR on its own. Adding classification, validation and learning to that would be effort spent on a problem you do not have.

When you need IDP

The picture changes as soon as the documents stop being uniform and the extracted data has to be reliable enough to post. That is the ordinary reality of accounts payable, where invoices come from hundreds of suppliers in every format, condition and channel, and each one has to end up as clean, correct data in the ERP.

You need IDP when several of these are true:

• Invoices arrive from many suppliers in layouts that keep changing, so per-template maintenance would never end.

• The data has to post into the ERP as labelled fields the ledger can use, with the right values in the right places.

• You match invoices against purchase orders and goods receipts, which depends on reliable line-level extraction.

• You want the clean majority of documents to move through without a person keying them, and only the exceptions to reach a human.

That last point is the goal most finance teams are really after. When extraction is accurate and validation is doing its job, the straightforward invoices post on their own and your team spends its time on the ones that genuinely need judgement. That is the foundation of touchless invoice processing, and it depends on the understanding that IDP adds on top of reading.

What 89% extraction accuracy means in practice

Stratas runs at 89% field-level extraction accuracy, measured on real customer documents in the condition they actually arrive. It is worth being precise about what a number like that describes, because accuracy figures in this market are often quoted without saying what they were measured on.

It is a day-one figure on real, mixed invoices. Point the system at a batch of documents from suppliers it has not been tuned for, in the layouts and conditions they actually arrive in, and roughly nine fields in ten come through correct with no template built in advance. As the model learns your suppliers and takes on corrections, the share that reads cleanly rises from there. A higher headline number measured on pristine, single-layout samples tells you far less about your Tuesday-morning inbox.

The number also drives how work is split. On unattended processing, documents that extract cleanly and pass validation post straight through to the ERP with no one touching them. On attended processing, anything that fails a check is routed to a person with the exact field flagged. Validation is what makes that workable: a field the system is unsure about, or one that fails a check against your records, becomes an exception a person reviews before it posts. The accuracy figure describes how much of the data reads cleanly; validation is there to stop what does not from posting unnoticed.