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.
Oh man, totally know what you mean here! We were literally pulling our hair out with this exact dilemma at my company not too long ago. We had that whole ‘traditional OCR vs. smarter IDP’ debate raging, trying to figure out how to handle all our incoming documents without it becoming a massive manual headache.
We actually spent a good chunk of time putting a few different solutions through their paces, comparing features, accuracy, and, let’s be honest, how user-friendly they felt. After all that, we ended up going with Lido.
And honestly? It’s been pretty smooth sailing ever since. Seriously, it’s handled everything we’ve thrown at it really well, and the setup was surprisingly straightforward. Trust me, after struggling with some of the other options, we’re super happy with that choice.
Oh man, okay, here’s one thing that honestly helped us out a ton with our whole document processing setup: we actually created a dedicated email address – something like invoices@company.com, you know? – and then we just trained all our vendors to send their invoices there. Seriously, it made our automation pipeline, like, ridiculously cleaner. Night and day.
Hey everyone, just a quick thought on this whole IDP vs. traditional OCR chat – something I’ve learned the hard way over the years. Honestly, scan quality is probably half the battle, maybe more, when it comes to getting good results, especially if you’re trying to do
Oh man, YES to UiPath! Seriously, I couldn’t agree more with what you’re saying about making the jump. We bit the bullet and made the switch to it about three months ago, and honestly, it’s already felt like a complete game-changer for our team.
Before, we were still messing around with a lot of traditional OCR solutions, trying to wrangle data from all sorts of incoming documents. But the sheer volume of emails we get, each with varying layouts and attachments needing data extracted? It was a nightmare. Our folks were spending hours just copy-pasting and manually typing things in, and the error rate was… well, let’s just say it kept us busy.
But honestly? The email capture and intelligent extraction features in UiPath alone have been worth the entire investment for us. It’s like night and day. It automatically pulls out the key info we need from those emails and their attachments, no more human eyes manually scanning every single one. That one capability has freed up so much time and drastically cut down on those frustrating manual errors. If you’re weighing up IDP versus sticking with plain old OCR, especially with email-heavy workflows, trust me, this is where the magic happens.