📑
PDF-Specialized OCR — Tools That Convert Complex Layouts, Tables, and Equations to Markdown
Marker, Docling, MinerU, Nougat, olmOCR — making PDFs readable for LLMs
Why Regular OCR Isn't Enough for PDFs
Tesseract on a PDF gives you text, but: multi-column layouts get mixed, table cells merge, equations break, headers mix into body text.
PDF-specialized tools understand layout and generate Markdown in correct reading order.
Tool Comparison
| Tool | Best For | CJK | Install |
|---|---|---|---|
| Marker | General purpose | Yes | pip install marker-pdf |
| Docling | Enterprise/RAG | Yes | pip install docling |
| MinerU | CJK documents | Best | uv pip install "mineru[all]" |
| Nougat | Academic papers | No | pip install nougat-ocr |
| PyMuPDF4LLM | Speed | Yes | pip install pymupdf4llm |
Key Concepts
1
Marker — pip install marker-pdf → marker_single doc.pdf for Markdown
2
MinerU — for CJK docs. Built-in PaddleOCR, best CJK layout handling
3
Nougat — academic papers only. Equations to LaTeX, references auto-handled
4
Docling — connects directly to RAG pipelines (LlamaIndex/LangChain)
Use Cases
RAG preprocessing — convert PDF to Markdown → chunk and store in vector DB
Paper analysis — extract equations, tables, references from academic PDFs
Multilingual docs — handle CJK layouts (vertical text, ruby, etc.) in PDFs