PDF Parsing API — PDFs to Clean Markdown at Scale

A REST API that turns PDFs into structured Markdown: OCR for scanned pages, LaTeX formula recognition, table extraction across page breaks, and multi-column reading order — the document parsing layer for RAG pipelines, document AI products, and batch archives.

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Quick start

Authenticate with a Bearer token or X-API-Key header and post a file:

# 1) Create job → 202 + job_... id
curl -X POST 'https://www.kolmopdf.com/api/v1/jobs/parse' \
  -H 'Authorization: Bearer sk-xxx' \
  -F 'file=@paper.pdf'

# 2) Poll status (or use webhook_url / SSE)
curl -H 'Authorization: Bearer sk-xxx' \
  'https://www.kolmopdf.com/api/v1/jobs/JOB_ID'

# 3) Download when status=succeeded
curl -L -H 'Authorization: Bearer sk-xxx' \
  'https://www.kolmopdf.com/api/v1/jobs/JOB_ID/download' -o result.zip

What the API handles

PDF OCR API — scanned and photographed documents parsed the same as native PDFs.
Table extraction — merged cells and tables that continue across pages come back as pipe tables.
Math and code — formulas as LaTeX, code blocks fenced with language hints.
Layout analysis — two- and three-column pages linearized into correct reading order.
Inline translation — optionally translate to 9 languages during the same parse.

Built for RAG and document AI

Retrieval quality is capped by ingestion quality. Feeding raw PDF text extraction into a vector database scrambles reading order and destroys tables — the model retrieves noise. Parsing PDFs to Markdown first gives your chunker real heading boundaries, keeps tables machine-readable, and preserves equations as tokens an LLM can reason about. Teams use this pdf parser API as the first stage before embedding, for LLM fine-tuning corpora, and for migrating PDF archives into knowledge bases.

Frequently asked questions

What does the PDF parsing API return?

GitHub-Flavored Markdown with heading hierarchy, pipe tables, fenced code blocks, and LaTeX math in $ / $$ delimiters — ready for vector databases, LLM context windows, or content pipelines.

Does the API OCR scanned PDFs?

Yes. The parser is a Visual Language Model that reads pages as images, so scanned documents and native PDFs go through the same endpoint with no separate OCR configuration. That is the ocr pdf / extract text from pdf path for pipelines — output is Markdown, not a raw pdf to text dump.

How is the API priced?

Per page parsed: 2 credits per page, or 3 credits per page with inline translation. New accounts include free credits, so you can integrate and test without a card.

Can it extract tables and math formulas?

Table extraction (including merged cells and tables spanning page breaks) and LaTeX formula recognition are the core of the model — it was trained on academic papers and technical documents where both are dense.

Is there a translation API as well?

Yes. A layout-preserving PDF translation API and a Markdown format conversion API share the same authentication. All three are covered in the API documentation.

Try it in the browser first: PDF to Markdown converter · Related: PDF translation, full API documentation.