The most useful thing anyone can do before introducing OCR into invoice processing is to write down what it will and will not change, and circulate it. Almost all dissatisfaction with these projects comes from an expectation nobody stated and nobody tested. The honest version is short: one job improves substantially, one new job appears, and three jobs are untouched.
Improves: keying and transcription errors
Typing supplier, date, number and totals disappears for most invoices, and with it a class of error that previously surfaced as supplier queries weeks later. For a team keying everything by hand this is immediate and measurable, and it is the benefit to lead with because it is the one you can demonstrate in a week.
Appears: the review queue
Somebody confirms the fields the software was unsure about. How big this task is depends on your document mix and on the confidence threshold you choose, and it is a real ongoing cost rather than a transitional one. Plan for it, staff it, and measure it, because an unstaffed review queue is where wrong data enters quietly.
Untouched: matching, coding, exceptions
The match compares documents rather than reading one. The account code is a judgement not printed on the invoice. And every conversation with purchasing, a site or a supplier about a difference happens exactly as before. If those are where your time goes, the honest answer is that capture is not your bottleneck and something else should be first.
Questions people ask about ocr for invoice processing
How do we set expectations internally?
Write down the three lists above, with your own measured minutes per invoice, and share them before the project starts. It costs an hour and prevents a year of disappointment.
Should we run a pilot?
Yes, on your own documents including the difficult ones, and measure review time rather than accuracy. Review time is what your team will actually experience.
What if accuracy is worse than promised?
Check the input first: resolution, orientation and channel consistency explain more variance than the engine does in most deployments.