Zerox: Document OCR with GPT-mini
Analysis
The article highlights a novel approach to document OCR using a GPT-mini model. The author found that this method outperformed existing solutions like Unstructured/Textract, despite being slower, more expensive, and non-deterministic. The core idea is to leverage the visual understanding capabilities of a vision model to interpret complex document layouts, tables, and charts, which traditional rule-based methods struggle with. The author acknowledges the current limitations but expresses optimism about future improvements in speed, cost, and reliability.
Key Takeaways
- •A new document OCR approach using GPT-mini is presented.
- •It outperforms existing solutions like Unstructured/Textract in some aspects.
- •The method leverages vision models for better handling of complex document layouts.
- •Current limitations include speed, cost, and non-determinism, but future improvements are anticipated.
““This started out as a weekend hack… But this turned out to be better performing than our current implementation… I've found the rules based extraction has always been lacking… Using a vision model just make sense!… 6 months ago it was impossible. And 6 months from now it'll be fast, cheap, and probably more reliable!””