I've built traditional OCR pipelines using LEAD Tools, Tesseract, and ABBYY. They work — until they don't. A slightly rotated scan, a different font weight, a handwritten field in the margin, or a table with merged cells, and the accuracy collapses. You end up with brittle regex patterns, endless exception handling, and a maintenance burden that grows with every new document format the client sends.

Then I started using AI vision models for document extraction. The difference is significant enough that I've replaced traditional OCR with AI-based extraction on every new project since. This post explains the approach, shows the C# implementation, and covers when it works best and where to be careful.

Why Traditional OCR Fails on Real-World Documents

Traditional OCR engines convert image pixels to text. They do this well when documents are clean, consistently formatted, and machine-printed.…