Marker - This Open-Source Tool will make your PDFs LLM Ready
AI Summary
Summary: Converting PDFs to Markdown for LLM Applications
- PDF Challenges for LLMs:
- PDFs are a prevalent format but difficult for LLMs due to complex structures, no standard layout, and varied encodings, fonts, and images.
- Converting PDFs to LLM-friendly formats is error-prone and cumbersome.
- Markdown Advantages:
- Markdown is easier for LLMs to process due to its ability to retain formatting like titles, headers, and tables.
- Conversion Tools:
- Paid options like Mathplix and open-source tools like NuGet (focused on academic documents) are available.
- Marker is a new, efficient open-source tool for converting PDFs to Markdown.
- Marker Performance:
- Faster and more accurate than NuGet.
- Preserves document structure better, as demonstrated with the “Think Python” book example.
- Marker Features:
- Supports various document types, optimized for books and scientific papers.
- Capable of removing headers, footers, and formatting tables and code blocks.
- Extracts and saves images, converts most equations to LaTeX.
- Runs on GPU, CPU, or MPS (Apple Silicon) and includes OCR capabilities.
- Marker Limitations:
- Not all equations and tables are converted perfectly.
- White spaces and line spans may not be respected.
- Usage Restrictions:
- Free for commercial use if the organization’s gross revenue is under 5 million in lifetime VC funding.
- Installation and Usage:
- Create a new conda environment and install PyTorch.
- Install Marker using
pip install markerpdf.- Convert single or multiple PDF files using respective commands.
- Optional OCR with additional installation.
- Practical Example:
- The process involves downloading OCR models, detecting layout, and extracting text.
- Outputs include extracted images, metadata JSON, and structured Markdown.
- Post-processing may be required for image and table accuracy.
- Conclusion:
- Marker is a powerful open-source tool for converting PDFs to structured Markdown, beneficial for LLM applications.
- Future content will cover data scraping from web pages.