Marker

Marker

★ 4.5 🔖 530 saves 💰 Free Data

Marker converts PDFs, EPUBs, and images into clean markdown, 10x faster than comparable open tools.

Overview

Marker is an open-source document-to-markdown converter built for speed and accuracy. It handles books, scientific papers, and reports with complex layouts, converting them to markdown with preserved headings, tables, equations, and images. It runs on GPU or CPU, is optimized for batch processing, and is a popular choice for preparing training and RAG data where markdown is the target format. Marker builds on the same model family as Surya for layout and OCR.

Marker’s speed advantage comes from a lean pipeline optimized around the layout and OCR models it shares with Surya. Teams preparing LLM training corpora use it to convert millions of pages into markdown cheaply, then filter and chunk the results downstream.

Key Features

  • PDF, EPUB, and image to markdown conversion
  • Preserves headings, tables, equations, and figures
  • Fast batch processing with GPU support
  • Strong handling of academic and technical layouts
  • Open-source and fully local execution
  • Optional hosted API through Datalab

Marker’s output quality matters most with technical content: math, tables, and code blocks survive conversion intact. This makes it the converter of choice for building knowledge bases from scientific and technical corpora.

Pricing

Marker is free and open source. Hosted conversion via Datalab is available as a paid service for teams.

Who Is It For

ML engineers preparing document datasets and RAG corpora who need fast, high-fidelity markdown at scale.

FAQ

Is Marker free to use?

Yes, the open-source version is completely free.

Does Marker offer an API?

Marker runs as a Python library and CLI; Datalab offers hosted API access for managed processing.

How does Marker compare to alternatives?

Where LlamaParse and Reducto are hosted platforms, Marker gives comparable conversion speed and quality in a free local tool.

Does Marker extract images?

Yes, figures and images are preserved and can be exported alongside the markdown text.

How does Marker handle tables?

Table structures are recognized and rendered as markdown tables, which keeps data usable for retrieval and analysis.

Can Marker process books?

Yes, long documents including books convert efficiently with batch processing.

What languages does OCR support?

OCR inherits Surya’s broad multilingual coverage for scanned pages.

Is Marker suitable for academic papers?

Yes, equations, tables, and figures in research papers convert cleanly.

Can it run in CI?

Yes, the CLI and Python API fit batch and pipeline automation.

Is Marker fast enough for large archives?

Yes, batch processing and GPU acceleration make it practical to convert millions of pages at low cost.

🚀 Visit Marker Official Site

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