Accuracy trade-offs when scanning driver's licenses from 50+ different layouts in one system

[Gerth Sniper]

Hey everyone, has anyone here dealt with building or tweaking a system that has to scan driver's licenses from like 50+ different countries or states all in one go? I'm running into this annoying thing where pushing for super high accuracy on weird layouts (think funky fonts, holograms messing with the text, or those reflective laminates) ends up making the whole process crawl, especially on mobile scans. But if I dial back the strict checks to keep it snappy for users, suddenly error rates jump and we get bad data slipping through. Last week I was testing some old European ones versus a few Asian formats and it felt like a constant tug-of-war—faster scans mean more manual fixes later, which defeats the point. Anyone found a sweet spot or some clever preprocessing tricks that help balance the two without everything falling apart? Curious what real-world trade-offs you've hit.

[Арно Дориан]

Funny how these ID layouts keep evolving—some countries update designs every few years just to add more anti-forgery tricks, and suddenly whatever system was humming along fine starts struggling again with glare from new holograms or shifted text blocks. I remember noticing this a while back when a batch of newer licenses from a couple places came in and the edges got all wavy from whatever plastic they used; it wasn't even about the OCR engine itself so much as how the physical document interacts with phone cameras in real life. Makes you wonder if we'll ever hit a point where standardization catches up, or if it's always gonna be this cat-and-mouse game with security features outpacing the scanning tech.

вівторок, 27 січня 2026 р. о 15:26:43 UTC+2 snipe...@gmail.com пише:

[Van Proft]

Yeah, I've bumped into exactly that headache a bunch when we rolled out something similar for a small verification flow. The layouts vary so wildly—even just within North America you get states with totally different field placements and security overlays that throw off basic OCR. What ended up working decently for us was leaning on tools that handle auto-classification first and then apply tailored extraction models per type, instead of one giant generic pass. It cuts down on those dumb misreads from trying to force everything through the same pipeline. Personally, I think https://ocrstudio.ai/id-scanner/ strikes a nice balance in that regard—it's on-premise, so no sending sensitive stuff anywhere, and it manages to pull clean data from a ton of global driver's licenses without choking on speed or needing perfect lighting every time. Not saying it's flawless (nothing is with these documents), but in my tests it felt less prone to the accuracy-vs-performance whiplash compared to some cloud-only options I've tried. Just my two cents from messing around with it on a side project.

вівторок, 27 січня 2026 р. о 15:34:41 UTC+2 arnoassa...@gmail.com пише: