OCR
Traditional OCR or AI OCR: what one transcribes, what the other understands
Traditional OCR or AI OCR: the difference is not reading quality, it is comprehension. An OCR (optical character recognition) transcribes characters. AI processing recognises the structure of the document, identifies the fields you asked for on a layout it has never seen, and returns a confidence level for every value.
The problem
The text is readable, the data is still missing
You are comparing two tools and you want to know which one reads better. That is the wrong question. Turning the image of a page into text is the first step, and what is actually extracted document by document is set out on the pillar page OCR for finance and procurement. What separates the two approaches comes afterwards: when you need to know which of the forty lines carries the unit price, and which item it belongs to.
On a real document, raw text is not enough. A twelve-column table becomes a run of words with no columns. An invoice-footer discount ends up glued to a total. An item reference split by a line break becomes two references. The file is readable; it is not usable.
That is why templates exist: a rectangle at this position for the invoice number, another for the total. A template works as long as the supplier changes nothing. It breaks the day they move their address block, add an environmental-levy column or switch to new billing software.
What traditional OCR does
It transcribes. That is already a great deal, and that is all.
- It converts the image of a page into characters, then words, then lines of text.
- It does not know that an invoice has a header, a table of lines and a footer, nor where to find the unit price.
- Without a template per layout, it returns a block of text in the order the characters appeared on the page.
- It does not say what it is sure of: a guessed character comes out with the same assurance as a crisp one.
What AI processing adds
Analysts covering this market call that layer IDP, for intelligent document processing. The acronym matters little; what it covers changes an accounts team’s daily work.
- It understands the structure of the document: header, body, table of lines, footer, attached annexes.
- It identifies the fields you asked for, even on a layout it has never encountered.
- It returns a confidence level for each value, and the exact position where it was read on the page.
- It holds up without a template, including when a supplier changes format without warning.
You describe the fields you want in plain language: no model to train, no set-up per supplier. The practical consequence is simple: the first document from a new supplier is handled like the thousandth from a familiar one.
The two approaches, point by point
No vendor names in this table, and no percentages: neither of those two grounds tells a decision-maker anything.
What comes out
Traditional OCR
Text, in the order the characters were met on the page.
Agent processing
Named fields, each tied to its position in the document.
Unknown layout
Traditional OCR
A template to create, then to maintain at every change of format.
Agent processing
Handled with no template and no model to train.
Table of lines
Traditional OCR
Columns lost, lines merged, descriptions cut in two.
Agent processing
A line stays a line: reference, description, quantity, unit, unit price, discount, tax rate.
Thirty-page document
Traditional OCR
Lines disappear silently, with no error shown.
Agent processing
The table stays continuous through to the last page.
Certainty on a value
Traditional OCR
No indication: everything comes out at the same level.
Agent processing
A score per field, and a human-review threshold that you set.
Several languages and currencies
Traditional OCR
Thousands separators and date formats taken literally.
Agent processing
Date formats, separators and currency symbols normalised on output.
Is the invoiced price the right one?
Traditional OCR
Out of scope.
Agent processing
Out of scope of extraction itself. That is the next step, and the subject of the section below.
What no OCR does, but the platform does
You are looking for an OCR. What you need is what comes after it.
Extraction is not the result. It is the first step. A price read correctly that breaches your contract is still a price paid in error.
Traditional OCR and AI processing stop at the same place: clean data. Neither knows that the unit price on line twenty-seven is four cents above the schedule signed in January, that the agreed volume tier was never applied, or that this invoice duplicates a document already settled. That work compares the value read against the negotiated terms: framework contract, rate schedule, purchase order, goods receipt.
Extract, compare, then prove.
What the next step produces
- The contract clause and the invoice line, highlighted side by side.
- No discrepancy is set aside in silence. Anything that matches no rule is raised, with its reason.
- It investigates, you decide.
Capture
Gather and consolidate all your procurement data.
Collection by dedicated mailbox, upload, SFTP, scan, API or ERP export. Reads every format (native PDF, scan, photo, spreadsheet, structured feed), then extracts line by line: supplier, references, quantities, unit prices, discounts, taxes, terms.
The end of re-entry. A procurement history you can finally query.
What Capture detects
- Long documents and multi-page tables
- Unknown layouts
- Several languages and currencies
- Poor scans and handwriting
The plan covers extraction. Checking your negotiated terms is the next step and is scoped with you.
See the volume tiers and the price per pageNothing is lost. Everything can be checked, everything can be proven.
- The contract clause and the invoice line, highlighted side by side.
- Every extracted value stays linked to the exact place in the document where it was read.
- The same case produces the same decision, today as in six months: the rules are applied deterministically.
- No discrepancy is set aside in silence. Anything that matches no rule is raised, with its reason.
- The agent records what it did, in the order it did it: who, what, how much, when.
Frequently asked questions
What is the difference between traditional OCR and AI OCR?
Traditional OCR transcribes characters. It does not know that an invoice has a header, lines and a footer, nor where to find the unit price. AI processing understands the structure of the document, identifies the requested fields even on an unknown layout, and returns a confidence level on every value.
Do we need a template per supplier?
No. You describe the fields you want, in plain language. No model to train, no set-up per layout, including when a supplier changes format without warning.
What is meant by IDP?
Intelligent document processing, or IDP, is the layer added on top of character recognition: understanding the structure, naming the expected fields, measuring confidence on each value. It is the vocabulary analysts use in this market; on this site we speak of agent processing, because the work does not stop at the document.
Does the data need to be perfectly clean?
No. The Capture agent is designed for heterogeneous documents and incomplete reference data; structuring is part of the deliverable.
Measurable impact in every environment
More than 5 million procurement documents analysed
Between 1 and 7% of margin recovered
on the scope analysed
From 15 to 45% of time given back to teams, per FTE
depending on the scope and on data maturity
Zylio fits into your existing ecosystem.
The ERP runs the process. Zylio handles the exception and recovers the value that escapes it: invoices without a purchase order, line-by-line price discrepancies, duplicates and overbilling, off-contract spend.
Your data under high security.
Zylio meets the most demanding standards, and nothing is committed without your approval.
- Certifications
- Hosting
- Encryption
- Access
Read next
- The OCR pillar: read, then verifyThe pillar: what is extracted, document by document, and what comes next.
- Invoice OCR accuracyA score per field and its position on the page, rather than one global percentage.
- Supplier invoice OCRThe fields genuinely expected on a purchase invoice, from header to footer.
- Cost of manual invoice entryThe other comparison: time per invoice and the volume threshold.
- Line-by-line extractionEvery item, with its reference, quantity, unit price and discount: the level where control actually happens.
See what this looks like on your own data
Twenty minutes, on a spend category of your choosing. We show you what the agents detect, with the evidence behind it.
- No commitment, on your own data
- Result in 3 weeks
- 20 minutes, no sales pitch
- Your data stays hosted in France
- No change of tool or process