High-quality PDF to markdown conversion for LLM pipelines.
Converts PDFs to clean markdown text — fast, accurate, and handles complex layouts with tables and images.
Marker is an open-source tool that converts PDF documents to clean markdown with a specific focus on accuracy and quality. Created by Vik Paruchuri (who also built Surya OCR), Marker combines deep learning models for layout detection, OCR, table recognition, and equation detection into a single pipeline optimized for producing high-fidelity markdown output.
Marker's pipeline is sophisticated for an open-source tool. It uses Surya for OCR and layout detection, a dedicated table recognition model, and a LaTeX equation detector. The pipeline identifies document regions, applies appropriate extraction for each (text, table, equation, figure), and assembles the output in reading order as clean markdown.
The markdown output quality is Marker's primary selling point. Headers are properly leveled, tables are formatted as markdown tables, equations are converted to LaTeX notation, and code blocks are identified and formatted. For RAG applications, this produces chunks that are significantly more readable and useful than raw text extraction.
Marker is designed for batch processing. The CLI tool processes individual PDFs or entire directories, outputting markdown files alongside any extracted images. Processing speed is reasonable — roughly 2-5 seconds per page on GPU, 10-20 seconds on CPU. GPU acceleration is strongly recommended for any non-trivial workload.
The tool excels at academic papers, technical documentation, and books — documents with clear structure, headings, and formatted content. It handles two-column layouts, footnotes, and page headers/footers with good accuracy. Table extraction is solid for simple-to-moderate tables but struggles with complex nested tables or heavily styled tables.
Marker's limitations are worth noting. It's primarily a CLI tool — no REST API, no cloud service, no real-time processing capability. Integration into applications requires calling it as a subprocess or using it as a library (less documented). It also doesn't provide structured output beyond markdown — no JSON with element types, no bounding boxes, no metadata beyond the markdown itself.
For teams that need high-quality PDF-to-markdown conversion for RAG knowledge bases, Marker is one of the best open-source options available. Its combination of layout detection, OCR, table recognition, and equation handling in a single package is hard to match at zero cost.
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Marker is a focused, open-source tool that does one thing exceptionally well: converting PDFs and other documents to clean Markdown. The output quality is excellent, particularly for preserving document structure, headings, lists, and code blocks. Being a single-purpose tool makes it easy to integrate into RAG pipelines. Limitations include slower processing speed than simpler extractors (it uses ML models), limited output format options (Markdown only), and no managed API — you must run it yourself.
Uses Surya models for detecting document regions: text blocks, headers, tables, figures, equations, code blocks, page headers, and footers. Handles multi-column layouts and complex page structures.
Use Case:
Converting a two-column research paper into single-column markdown with correct reading order and section hierarchy.
Integrated Surya OCR engine optimized for document text recognition. Supports 90+ languages and handles mixed-language documents. Better accuracy than Tesseract for most document types.
Use Case:
Processing scanned technical documents in multiple languages where Tesseract OCR produces too many errors.
Detects tables and converts them to properly formatted markdown tables with column alignment. Handles simple and moderately complex table structures.
Use Case:
Converting a technical specification PDF with comparison tables into markdown where table data is preserved in a readable format.
Identifies mathematical equations in documents and converts them to LaTeX notation in the markdown output. Handles both inline and display equations.
Use Case:
Converting a mathematics textbook or research paper to markdown where equations need to be preserved for rendering or search.
Command-line tool for processing individual PDFs or entire directories. Outputs markdown files and extracted images organized by document. Supports configurable processing parameters.
Use Case:
Converting an entire digital library of 1,000 PDFs to markdown files for building a comprehensive RAG knowledge base.
Detects and extracts figures and images from documents, saving them as separate files and inserting markdown image references in the output.
Use Case:
Preserving diagrams and charts from technical documentation as accessible images alongside the markdown text for a documentation site.
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View Pricing Options →Converting academic papers and technical documentation to markdown for RAG knowledge bases where equation and table fidelity matter
Batch processing PDF libraries into clean markdown for static documentation sites or search indexes
Processing research papers with complex layouts (multi-column, equations, figures) that break simpler extraction tools
Teams needing high-quality PDF-to-markdown conversion in a self-hosted, open-source pipeline
Marker works with these platforms and services:
We believe in transparent reviews. Here's what Marker doesn't handle well:
Both produce high-quality output from PDFs. Marker focuses specifically on markdown output and excels at equations and code blocks. Docling provides richer structured output (DoclingDocument) with element types and bounding boxes. For markdown-based RAG pipelines, Marker's output is often cleaner. For structured processing, Docling is more flexible.
Not technically — it runs on CPU — but practically, yes. CPU processing takes 10-20 seconds per page, making batch processing extremely slow. With a GPU, expect 2-5 seconds per page. For anything beyond a few documents, GPU is essential.
Yes, through its integrated Surya OCR. Scanned documents at 300+ DPI produce good results. Lower-quality scans or documents with handwriting will have reduced accuracy.
Marker can be used as a Python library (from marker.converters.pdf import PdfConverter) though this is less documented than the CLI. Most teams either use the library directly or shell out to the marker CLI tool.
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