Accurately extract text from research literature PDFs with Nougat-OCR and Docling
How to Tame the PDF Beast with Nougat-OCR and Docling.
Hello fellow datanistas!
Have you ever struggled with extracting meaningful data from PDFs, especially when they contain complex elements like equations and tables? You're not alone, and I've been on a journey to find a solution.
Parsing published literature into plain text is a task that seems deceptively simple. In reality, PDFs can be notoriously difficult to work with, especially when they include elements like equations, tables, and figures. If you're working with large language models (LLMs) or just trying to extract data for analysis, the standard text extraction tools often leave significant amounts of useful context behind. Recently, I explored two tools—Nougat-OCR by Facebook Research and Docling by IBM—to address this problem more effectively.
Nougat-OCR excels at extracting equations and tables, while Docling is perfect for figures. By combining these tools, I've developed a workflow that captures all critical components of a PDF with minimal loss of context. This approach is scalable and can be deployed on platforms like Modal for enhanced performance.
For a detailed walkthrough of how these tools work and how you can implement them, check out my full blog post here.
Parsing PDFs into structured plain text is more than just a convenience; it's a necessity when working with LLMs or conducting scientific analysis. By combining Nougat-OCR for text-based elements and Docling for visual content, you can extract high-quality data from published literature.
I invite you to read the full blog post to learn more about these tools and how they can enhance your data extraction process. If you find it helpful, please consider forwarding it to others who might benefit from it.
Happy coding!
Eric