Convert PDF to Markdown
Extract text, headings, tables, and structure from PDF files into well-formatted Markdown. Perfect for research papers, reports, and documentation — and for feeding documents to Claude, NotebookLM, and RAG pipelines.
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Open converter →Why convert PDF to Markdown?
- Import research papers and reports into Obsidian or Notion
- Feed PDF documentation into RAG pipelines and AI workflows
- Publish PDF content on GitHub or static site generators
How the PDF to Markdown conversion works
PDFs don't store headings, lists, or tables — they store characters positioned on a page. Our converter uses a layout-aware extraction engine (OpenDataLoader) that reconstructs the document's logical structure: it detects headings from font size and weight, rebuilds the correct reading order across multi-column layouts, and turns detected table grids into proper Markdown pipe tables instead of jumbled rows of text.
Before converting, we check whether the PDF actually contains a text layer. Digitally-created PDFs (exported from Word, LaTeX, Google Docs) convert directly. Scanned PDFs — photocopies, faxes, old contracts — have no text layer at all, so they're routed through AI-powered OCR that reads each page as an image and reconstructs the Markdown. OCR is a per-page AI operation, so it's part of the Pro plan.
What survives the conversion — and what to watch for
Headings, paragraphs, bullet and numbered lists, and most tables come through cleanly. A few PDF quirks are worth knowing: images and figures are dropped (you get the text, not embedded graphics), footnotes usually land inline near the end of the page they belong to, and deeply nested or merged-cell tables get flattened into simpler grids. Very large, graphics-heavy PDFs can exceed processing limits — splitting them into chapters converts more reliably.
Your files are processed for the conversion and immediately discarded — nothing is stored, indexed, or used for training. The free tier converts files up to 25MB (3 per day); Pro raises that to 100MB with unlimited conversions and scanned-PDF OCR.
Why Markdown is the right target for AI workflows
Markdown is the format LLMs read best: headings become document structure the model can navigate, tables stay machine-parseable, and all the PDF rendering noise disappears. Converted output pastes straight into Claude or ChatGPT, uploads cleanly to NotebookLM, and chunks naturally along heading boundaries in RAG pipelines — which is exactly why researchers and AI engineers are the heaviest users of this tool.
Example: what the Markdown output looks like
A research-paper page with a title, abstract, and results table converts to:
# Attention-Efficient Retrieval for Long Documents
## Abstract
We propose a retrieval method that reduces context usage by 43%
while preserving answer accuracy on three benchmarks.
## Results
| Benchmark | Baseline | Ours |
| --- | --- | --- |
| NQ-Long | 61.2 | 63.8 |
| HotpotQA | 58.7 | 59.9 |Frequently asked questions
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