PDF to Markdown for LLM & RAG (Tables + OCR)

Convert PDF files to clean, LLM-ready Markdown: headings, paragraphs, Markdown tables, and OCR for scanned pages. Built for RAG pipelines, AI agents, document search and data extraction.

• Two-column papers read in the right order (left column, then right)
• Headings detected from font sizes (#, ##, ###), paragraphs re-joined, hyphenated words fixed
• Tables converted to real Markdown tables (layout boxes are ignored)
• Scanned PDFs: pages without a text layer are OCR'd with Tesseract (English, French, German, Spanish, Italian, Portuguese, Dutch)
• Bulk: give hundreds of URLs, they're processed in parallel
• Fair pricing: you pay only for PDFs that convert successfully. Failed downloads cost nothing.

How to use

1. Paste one or more direct PDF links into PDF URLs (or upload a text file with one URL per line).
2. Keep the defaults, or turn off OCR / tables, limit pages, pick the OCR language.
3. Click Start. Each PDF becomes one dataset item. Export as JSON, CSV, Excel, or fetch through the API.

Input example

{
  "pdfUrls": [{ "url": "https://arxiv.org/pdf/1706.03762" }],
  "extractTables": true,
  "ocrScannedPages": true,
  "ocrLanguage": "eng",
  "maxPages": 0
}

Output example

{
  "url": "https://arxiv.org/pdf/1706.03762",
  "status": "ok",
  "title": "Attention Is All You Need",
  "pageCount": 15,
  "ocrPages": 0,
  "tableCount": 0,
  "charCount": 40211,
  "markdown": "# Attention Is All You Need\n\nAshish Vaswani ...\n\n### Abstract\n\nThe dominant sequence transduction models ..."
}

Failed PDFs come back with "status": "error" and an error message (not charged).

Pricing

Pay per event:

Event	Price
PDF converted (text layer)	$0.003 per PDF
OCR page (scanned page)	$0.01 per page

1,000 text PDFs cost $3. Set a maximum cost per run in the run options and the Actor stops cleanly when it is reached.

Use with AI agents and APIs

Call it from the Apify API, Python/JS clients, Make, Zapier, n8n, LangChain, LlamaIndex, or through the Apify MCP server, so your agent can read any PDF by URL.

curl -X POST "https://api.apify.com/v2/acts/<ACTOR_ID>/run-sync-get-dataset-items?token=<TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"pdfUrls":[{"url":"https://arxiv.org/pdf/1706.03762"}]}'

Limits

• Max 100 MB per PDF, 5 minutes per PDF.
• Password-protected PDFs aren't supported.
• Complex multi-level tables (e.g. tax tables with merged cells) may come out simplified. Tables drawn without lines are kept as text.
• Two-column layouts (papers, journals) are detected and read column by column; full-width titles and figures stay in place.

FAQ

Is my data stored? Only in your own Apify run storage, which follows your retention settings.

Can I convert local files? Upload them to any public URL (or Apify key-value store) and pass the link.

Found a PDF that converts badly? Open an issue on the Actor's Issues tab with the link and it will be fixed.

Related Actors

• Bulk Image OCR: text from screenshots, scans and photos
• Word, PowerPoint & Excel to Markdown: DOCX, PPTX, XLSX, HTML, EPUB to Markdown

Free browser version

Converting just one file? Use the free in-browser converter at hackthehydra.com: no upload, no account.

More ways to use it

• REST API without an Apify account: the same converters are available on RapidAPI as the Document to Markdown API (PDF, DOCX, PPTX, XLSX, images; free plan included).
• Free in your browser: hackthehydra.com has no-upload converters for PDF, Word, Excel, PowerPoint and images.

Use cases

• LLM and RAG: turn papers, manuals and reports into Markdown chunks for vector stores, with headings and tables preserved.
• Research: bulk-convert arXiv or publisher PDFs into searchable text for literature reviews.
• Operations and SEO: extract text from supplier catalogues, whitepapers and scanned contracts (OCR).

Integrations

Python (Apify client):

from apify_client import ApifyClient

client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("extas_corp/pdf-to-markdown").call(run_input={"pdfUrls": [{"url": "https://arxiv.org/pdf/1706.03762"}]})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["markdown"][:500])

LangChain / RAG (turn each result into a Document):

from apify_client import ApifyClient
from langchain_core.documents import Document

client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("extas_corp/pdf-to-markdown").call(run_input={"pdfUrls": [{"url": "https://arxiv.org/pdf/1706.03762"}]})
docs = [Document(page_content=i["markdown"], metadata={"source": i.get("url")})
        for i in client.dataset(run["defaultDatasetId"]).iterate_items() if i.get("markdown")]

Make, Zapier, n8n: add the Apify app, choose Run Actor (or Run Actor and get dataset items), set the Actor to extas_corp/pdf-to-markdown and map the JSON input. The same Actor can be started on a schedule from Apify itself, with a webhook to your own endpoint when the run finishes.