What AI-based OCR can do
Ordinary OCR recognises characters but gets lost the moment the layout is unfamiliar. A model understands meaning: it knows an invoice has a supplier, line items, a total and a tax figure, and it finds them even in a form it has never seen. That is why it handles a stream of documents from dozens of different counterparties noticeably better.
Where accuracy is high and where checking is needed
| Type of data | Reliability | What to do |
|---|---|---|
| Printed text, registration details | high | spot checks |
| Totals and tax amounts | high, but critical | a control check is mandatory |
| Line-item tables | depends on the layout | verify the totals |
| Handwriting, stamps | lower | manual review |
| Poor scans | low | rescan them |
How it fits into accounting
Recognition is only the first step. The data then goes into the accounting system, and that is where control matters most: keep a reconciliation step between the extracted values and the posted entry, rather than letting anything flow straight through.
Where the documents get processed
Invoices, bills and contracts are sensitive material. With a self-hosted model the recognition runs on your own server and the scans never reach an external cloud. On the private setup, see an AI server on your own hardware.
Where to start
- •Fix the scanning first — flat, legible scans,
- •Run recognition on a single document type, invoices for instance,
- •Keep the control check on totals and registration details,
- •Expand to bills and contracts once it has settled in.
To take manual paperwork entry off the team without turning it into a black box, see the Stitex AI Bookkeeper — recognition with verification built in.
Frequently asked questions
How is AI-based OCR better than the classic kind?
Classic OCR simply reads characters. A model understands the structure of the document: which part is the supplier, which is the total, which is the VAT — even on an unfamiliar layout. That is why it copes far better with invoices arriving in dozens of different formats.
Can we trust the numbers without checking?
On the critical fields — totals, bank and registration details — a human check or a reconciliation against a reference is needed. The model speeds entry up enormously, but the cost of an error in accounting is high, so the control check stays.
Does it read handwriting and stamps?
Printed text, confidently. Handwriting and poor scans, much less so — quality there depends on the source. A bad scan stays bad for any system, which is why scanning discipline matters.
Will document data leak?
With a self-hosted model, recognition happens on your own server and scans of contracts and invoices never reach an external cloud. For accounting documents that is a fundamental requirement.