This keeps coming up in vendor calls and I’m genuinely confused. Everyone’s pitching “IDP” now like it’s this revolutionary thing — but when I ask what makes it different from OCR, I usually get a lot of buzzwords. Doesn’t traditional OCR already extract text from documents? What am I actually missing here?
Totally understand the confusion — the marketing around this stuff is thick. But honestly, the difference is more significant than the buzzwords make it sound.
Traditional OCR does one thing: it reads text out of an image and dumps it out as raw text. That’s it. Everything after that — finding the invoice number, figuring out which number is the total vs. the tax, validating that a date is a date — that’s on you or your dev team. It’s rule-based, it’s brittle, and it falls apart the moment a document doesn’t match what it was configured for.
IDP layers machine learning and NLP on top of that. Instead of raw text, you get structured, classified data. The system understands what kind of document it’s looking at, what the fields mean, and can handle real-world variation without someone manually writing rules for every edge case.
Practical example: run an invoice through traditional OCR and you get a blob of text. Run it through an IDP system and you get invoice number, vendor name, line items, totals, currency, due date — already parsed and validated, ready to drop into your ERP or spreadsheet.
With traditional OCR, a developer has to write all the parsing logic, maintain it when formats change, and handle the inevitable edge cases. It’s a constant maintenance tax. IDP handles that at the platform level.
I’ve tried a few tools in this space — Lido included — and the ones that are actually doing IDP (not just calling OCR “intelligent”) save a ton of cleanup work downstream. The capability gap is real. For most companies, it makes more sense to skip traditional OCR entirely and just start with IDP. The cost difference has come way down, and the labor you save on data wrangling and integration makes up for it pretty quickly.
Just to throw some real numbers out there — we’re about 350 people and push through roughly 800 invoices a month. Tesseract was our first stop because, honestly, free is hard to argue with when you’re trying to get budget approved. But the accuracy on anything that wasn’t a clean, flat PDF was pretty rough. We were sitting around 60-70% on the messier stuff, which just created more manual work than we started with. Switched to ABBYY a while back and we’re consistently hitting 95%+ now. Night and day difference, especially on the crumpled or faxed documents.
Mostly agree with everything here, but I’d pump the brakes a little on the ‘no templates needed’ thing. That’s true in the sense that you’re not building rigid field maps, but these AI tools still need time to learn your specific documents. We had to go through a feedback period of a few weeks before accuracy really leveled out. It’s definitely less painful than templates, don’t get me wrong — but if you go in expecting true plug-and-play you might be disappointed on day one.