I just released a curated collection of Jupyter notebooks showcasing and comparing powerful OCR and document parsing tools! This project dives into real-world document understanding text, tables, and charts using top open-source and AI-powered models like PaddleOCR, Florence 2, Gemini 2.5 Pro, Pix2Text, Docling, Dolphin, LLaMA3 Vision, and more.
Each notebook provides a hands-on example, making it easier to evaluate performance across different scenarios and tools. Whether you’re working on invoice automation, PDF parsing, or document intelligence, you’ll find useful insights here.
Check it out on GitHub 👉 <a href="https://github.com/AdemBoukhris457/Docs_Parsing_Techniques" target="_blank" rel="noopener nofollow">https://github.com/AdemBoukhris457/Docs_Parsing_Techniques</a>
Feedback and contributions are welcome!

Deep Learning is a subset of Machine Learning that uses artificial neural networks to model complex patterns in data. Inspired by the human brain, it excels in tasks like image recognition, natural language processing, and predictive analytics.

Deep Learning

A curated collection of 12 Jupyter notebooks demonstrates and compares various OCR and document parsing tools including PaddleOCR, Florence 2, Gemini 2.5 Pro, Pix2Text, Docling, and LLaMA3 Vision. The notebooks provide hands-on examples for extracting text, tables, and charts from documents, helping developers evaluate different tools for applications like invoice automation and PDF parsing.

12 Hands-On Notebooks for Modern Document Parsing with AI & OCR