Beyond OCR: How Modern AI Reads Documents Like a Human
Old-school OCR struggled with tables and handwriting. New Vision-Language Models (VLMs) understand context. Here is the difference.
PO2Order Team
Editor in Chief
For 20 years, “data extraction” meant OCR (Optical Character Recognition).
It was a dumb technology. It looked at a pixel and guessed “That is the letter A.” It didn’t know what an “A” was, or that it was part of a word, or that the word was inside a column labeled “Quantity.”
This is why traditional OCR fails on:
- Multi-line descriptions.
- Skewed scans.
- Complex tables without gridlines.
Enter the Vision-Language Model (VLM)
The new generation of AI from providers like OpenAI, Anthropic, Google, and x.ai works differently. It doesn’t just “see pixels.” It reads.
It looks at a document the way a human does:
- Context: “This looks like a Purchase Order.”
- Structure: “This big bold number at the top is probably the PO Number.”
- Semantics: “The column labeled ‘Qty’ contains numbers. The column labeled ‘Description’ contains text.”
Why This Matters for B2B
B2B documents are messy. Every customer uses a different template. Some are Excel exports; some are photos of a napkin.
- Old OCR: Requires you to build a “Template” for every single customer. If the customer moves a column, the template breaks.
- New AI: Zero templates. It just reads. If the “Total” moves to the bottom left, the AI finds it, just like you would.
This shift from Template-Based OCR to Semantic AI is what makes tools like PO2Order possible today when they were impossible 5 years ago. We have finally taught computers to read.
