LLM-Based OCR Is Significantly Outperforming Traditional ML-Based OCR
LLM-Based OCR Is Significantly Outperforming Traditional ML-Based OCR
Traditional OCR - Tesseract, Google Vision API, AWS Textract - works by detecting characters in images. It is fast and cheap but struggles with context. It can read "Submit" on a button but does not know it is a button. It can read a table but does not understand the relationships between cells.
LLM-based OCR changes the game by understanding what it is reading.
Why LLM Vision Wins
LLM vision models do not just detect characters. They understand the visual structure of what they are looking at. They know that text inside a rounded rectangle is probably a button. They know that aligned columns of text are probably a table. They know that smaller gray text below larger black text is probably a description.
This contextual understanding means:
- Higher accuracy on messy inputs - handwriting, low contrast, unusual fonts
- Structural understanding - the model returns "a button labeled Submit" not just the string "Submit"
- Multi-language handling without language-specific model training
- Layout comprehension - understanding reading order, columns, sidebars
The Accessibility API Combo
The best results come from combining accessibility APIs with LLM vision. The accessibility API provides the structured UI tree - button labels, text field values, menu items. LLM vision fills in the gaps where the API has no coverage - images, custom-drawn UI, PDF content.
This combo gives you:
- Structured data where it exists (accessibility API)
- Visual understanding where it does not (LLM vision)
- Cross-validation between both sources for higher confidence
Practical Impact
For desktop agents, this means they can read and understand any application on screen, not just ones with good accessibility support. Legacy apps with custom UI, PDF viewers, image editors - all become readable. The agent can interact with applications that were previously opaque.
Fazm is an open source macOS AI agent. Open source on GitHub.