I’m excited to share a new project I’ve been working on: Documents-Parsing-Lab
This project is not a benchmark, but an exploration lab — built around a collection of Jupyter notebooks that test different AI models on various document structures. The idea is simple: let users see the parsing results directly, compare approaches, and understand where each model shines (and where it struggles).
🔍 What’s Inside?
Each notebook applies one or more models to different real-world document types, including:
- PDFs
- Tables
- Charts & Figures
- Complex layouts
⚡ Models Explored So Far
-
OCR & Vision Models
- Dolphin (ByteDance)
- Typhoon_OCR_7B
- MonkeyOCR
- PaddleOCR
-
Table Structure Recognition
- UniTable
- TableFormer
- Microsoft Table Transformer
(…and more to come!)
✨ Why This Project?
There are lots of benchmarks out there — but in practice, people want to see how models handle real documents. This lab is meant to be a hands-on exploration space where you can:
- Run the notebooks yourself
- Compare outputs across models
- Understand limitations before using them in production
📈 What’s Next?
More models and examples will be added over time. The goal is to keep expanding this into a growing resource for anyone interested in:
- OCR
- Document AI
- Structured data extraction
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