Common OCR Errors (And How to Fix Them)
TL;DR
OCR is fragile. Low resolution, tilted scans, and complex tables break standard tools. Here is how to fix them before they break your workflows.
Even the best OCR engines can struggle with low-resolution scans, tilted pages, or complex multi-column layouts. If you've ever seen a "0" turned into an "O" or a column of numbers shifted to the left, you've experienced OCR failure.
1. The "O" vs "0" Problem
In poor lighting or low-DPI scans, OCR can easily mistake a zero for the letter O.
The Fix: Extractify uses post-processing validation. If a field is labeled "Amount," our engine knows that an "O" is statistically impossible and automatically corrects it to a "0" or flags it for verification.
2. Tilted Layouts (Skewing)
If a document is scanned at an angle, the extraction logic might fail to align columns.
The Fix: Our pre-processing stage automatically detects and corrects document rotation (de-skewing) before the engine even looks at the text.
3. Complex Tables: The Final Boss
Nested tables and merged cells are where standard AI tools often hallucinate.
"See how we handle complex Invoice tables"
Get Started →Is OCR alone enough for automation?
Short answer: No. OCR gives you raw strings. Automation requires Intelligence. Using OCR without a validation layer (like Extractify) is like hireing someone to read your mail but not to actually file it correctly.
Why Data Errors Are Expensive
A single misread decimal point on a shipping manifest can lead to customs delays, fines, and lost customers. Automating document parsing isn't just about speed; it's about accuracy as a service.