Marker

Marker converts documents to markdown, JSON, and HTML quickly and accurately.

Converts PDF, image, PPTX, DOCX, XLSX, HTML, EPUB files in all languages
Formats tables, forms, equations, inline math, links, references, and code blocks
Extracts and saves images
Removes headers/footers/other artifacts
Extensible with your own formatting and logic
Optionally boost accuracy with LLMs
Works on GPU, CPU, or MPS

Performance

Marker benchmarks favorably compared to cloud services like Llamaparse and Mathpix, as well as other open source tools.

The above results are running single PDF pages serially. Marker is significantly faster when running in batch mode, with a projected throughput of 122 pages/second on an H100 (.18 seconds per page across 22 processes).

See below for detailed speed and accuracy benchmarks, and instructions on how to run your own benchmarks.

Hybrid Mode

For the highest accuracy, pass the --use_llm flag to use an LLM alongside marker. This will do things like merge tables across pages, handle inline math, format tables properly, and extract values from forms. It can use any gemini or ollama model. By default, it uses gemini-2.0-flash. See below for details.

Here is a table benchmark comparing marker, gemini flash alone, and marker with use_llm:

As you can see, the use_llm mode offers higher accuracy than marker or gemini alone.

Examples

PDF	File type	Markdown	JSON
Think Python	Textbook	View	View
Switch Transformers	arXiv paper	View	View
Multi-column CNN	arXiv paper	View	View

Commercial usage

I want marker to be as widely accessible as possible, while still funding my development/training costs. Research and personal usage is always okay, but there are some restrictions on commercial usage.

The weights for the models are licensed cc-by-nc-sa-4.0, but I will waive that for any organization under $5M USD in gross revenue in the most recent 12-month period AND under $5M in lifetime VC/angel funding raised. You also must not be competitive with the Datalab API. If you want to remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the options here.

Hosted API

There's a hosted API for marker available here:

Supports PDFs, word documents, and powerpoints
1/4th the price of leading cloud-based competitors
High uptime (99.99%), quality, and speed (around 15 seconds to convert a 250 page PDF)

Community

Discord is where we discuss future development.

Installation

You'll need python 3.10+ and PyTorch. You may need to install the CPU version of torch first if you're not using a Mac or a GPU machine. See here for more details.

Install with:

pip install marker-pdf

If you want to use marker on documents other than PDFs, you will need to install additional dependencies with:

pip install marker-pdf[full]

Usage

First, some configuration:

Your torch device will be automatically detected, but you can override this. For example, TORCH_DEVICE=cuda.
Some PDFs, even digital ones, have bad text in them. Set the force_ocr flag to ensure your PDF runs through OCR, or the strip_existing_ocr to keep all digital text, and strip out any existing OCR text.

Interactive App

I've included a streamlit app that lets you interactively try marker with some basic options. Run it with:

pip install streamlit
marker_gui

Convert a single file

marker_single /path/to/file.pdf

You can pass in PDFs or images.

Options:

--output_dir PATH: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR.
--output_format [markdown|json|html]: Specify the format for the output results.
--paginate_output: Paginates the output, using \n\n{PAGE_NUMBER} followed by - * 48, then \n\n
--use_llm: Uses an LLM to improve accuracy. You must set your Gemini API key using the GOOGLE_API_KEY env var.
--redo_inline_math: If you want the highest quality inline math conversion, use this along with --use_llm.
--disable_image_extraction: Don't extract images from the PDF. If you also specify --use_llm, then images will be replaced with a description.
--page_range TEXT: Specify which pages to process. Accepts comma-separated page numbers and ranges. Example: --page_range "0,5-10,20" will process pages 0, 5 through 10, and page 20.
--force_ocr: Force OCR processing on the entire document, even for pages that might contain extractable text.
--strip_existing_ocr: Remove all existing OCR text in the document and re-OCR with surya.
--debug: Enable debug mode for additional logging and diagnostic information.
--processors TEXT: Override the default processors by providing their full module paths, separated by commas. Example: --processors "module1.processor1,module2.processor2"
--config_json PATH: Path to a JSON configuration file containing additional settings.
--languages TEXT: Optionally specify which languages to use for OCR processing. Accepts a comma-separated list. Example: --languages "en,fr,de" for English, French, and German.
config --help: List all available builders, processors, and converters, and their associated configuration. These values can be used to build a JSON configuration file for additional tweaking of marker defaults.
--converter_cls: One of marker.converters.pdf.PdfConverter (default) or marker.converters.table.TableConverter. The PdfConverter will convert the whole PDF, the TableConverter will only extract and convert tables.
--llm_service: Which llm service to use if --use_llm is passed. This defaults to marker.services.gemini.GoogleGeminiService.
--help: see all of the flags that can be passed into marker. (it supports many more options then are listed above)

The list of supported languages for surya OCR is here. If you don't need OCR, marker can work with any language.

Convert multiple files

marker /path/to/input/folder --workers 4

marker supports all the same options from marker_single above.
--workers is the number of conversion workers to run simultaneously. This is set to 5 by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average.

Convert multiple files on multiple GPUs

NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out

NUM_DEVICES is the number of GPUs to use. Should be 2 or greater.
NUM_WORKERS is the number of parallel processes to run on each GPU.

Use from python

See the PdfConverter class at marker/converters/pdf.py function for additional arguments that can be passed.

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.output import text_from_rendered

converter = PdfConverter(
    artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")
text, _, images = text_from_rendered(rendered)

rendered will be a pydantic basemodel with different properties depending on the output type requested. With markdown output (default), you'll have the properties markdown, metadata, and images. For json output, you'll have children, block_type, and metadata.

Custom configuration

You can pass configuration using the ConfigParser. To see all available options, do marker_single --help.

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.config.parser import ConfigParser

config = {
    "output_format": "json",
    "ADDITIONAL_KEY": "VALUE"
}
config_parser = ConfigParser(config)

converter = PdfConverter(
    config=config_parser.generate_config_dict(),
    artifact_dict=create_model_dict(),
    processor_list=config_parser.get_processors(),
    renderer=config_parser.get_renderer(),
    llm_service=config_parser.get_llm_service()
)
rendered = converter("FILEPATH")

Extract blocks

Each document consists of one or more pages. Pages contain blocks, which can themselves contain other blocks. It's possible to programmatically manipulate these blocks.

Here's an example of extracting all forms from a document:

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.schema import BlockTypes

converter = PdfConverter(
    artifact_dict=create_model_dict(),
)
document = converter.build_document("FILEPATH")
forms = document.contained_blocks((BlockTypes.Form,))

Look at the processors for more examples of extracting and manipulating blocks.

Other converters

You can also use other converters that define different conversion pipelines:

Extract tables

The TableConverter will only convert and extract tables:

from marker.converters.table import TableConverter
from marker.models import create_model_dict
from marker.output import text_from_rendered

converter = TableConverter(
    artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")
text, _, images = text_from_rendered(rendered)

This takes all the same configuration as the PdfConverter. You can specify the configuration force_layout_block=Table to avoid layout detection and instead assume every page is a table. Set output_format=json to also get cell bounding boxes.

You can also run this via the CLI with

marker_single FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter --output_format json

Output Formats

Markdown

Markdown output will include:

image links (images will be saved in the same folder)
formatted tables
embedded LaTeX equations (fenced with $$)
Code is fenced with triple backticks
Superscripts for footnotes

HTML

HTML output is similar to markdown output:

Images are included via img tags
equations are fenced with <math> tags
code is in pre tags

JSON

JSON output will be organized in a tree-like structure, with the leaf nodes being blocks. Examples of leaf nodes are a single list item, a paragraph of text, or an image.

The output will be a list, with each list item representing a page. Each page is considered a block in the internal marker schema. There are different types of blocks to represent different elements.

Pages have the keys:

id - unique id for the block.
block_type - the type of block. The possible block types can be seen in marker/schema/__init__.py. As of this writing, they are ["Line", "Span", "FigureGroup", "TableGroup", "ListGroup", "PictureGroup", "Page", "Caption", "Code", "Figure", "Footnote", "Form", "Equation", "Handwriting", "TextInlineMath", "ListItem", "PageFooter", "PageHeader", "Picture", "SectionHeader", "Table", "Text", "TableOfContents", "Document"]
html - the HTML for the page. Note that this will have recursive references to children. The content-ref tags must be replaced with the child content if you want the full html. You can see an example of this at marker/output.py:json_to_html. That function will take in a single block from the json output, and turn it into HTML.
polygon - the 4-corner polygon of the page, in (x1,y1), (x2,y2), (x3, y3), (x4, y4) format. (x1,y1) is the top left, and coordinates go clockwise.
children - the child blocks.

The child blocks have two additional keys:

section_hierarchy - indicates the sections that the block is part of. 1 indicates an h1 tag, 2 an h2, and so on.
images - base64 encoded images. The key will be the block id, and the data will be the encoded image.

Note that child blocks of pages can have their own children as well (a tree structure).

{
      "id": "/page/10/Page/366",
      "block_type": "Page",
      "html": "<content-ref src='/page/10/SectionHeader/0'></content-ref><content-ref src='/page/10/SectionHeader/1'></content-ref><content-ref src='/page/10/Text/2'></content-ref><content-ref src='/page/10/Text/3'></content-ref><content-ref src='/page/10/Figure/4'></content-ref><content-ref src='/page/10/SectionHeader/5'></content-ref><content-ref src='/page/10/SectionHeader/6'></content-ref><content-ref src='/page/10/TextInlineMath/7'></content-ref><content-ref src='/page/10/TextInlineMath/8'></content-ref><content-ref src='/page/10/Table/9'></content-ref><content-ref src='/page/10/SectionHeader/10'></content-ref><content-ref src='/page/10/Text/11'></content-ref>",
      "polygon": [[0.0, 0.0], [612.0, 0.0], [612.0, 792.0], [0.0, 792.0]],
      "children": [
        {
          "id": "/page/10/SectionHeader/0",
          "block_type": "SectionHeader",
          "html": "<h1>Supplementary Material for <i>Subspace Adversarial Training</i> </h1>",
          "polygon": [
            [217.845703125, 80.630859375], [374.73046875, 80.630859375],
            [374.73046875, 107.0],
            [217.845703125, 107.0]
          ],
          "children": null,
          "section_hierarchy": {
            "1": "/page/10/SectionHeader/1"
          },
          "images": {}
        },
        ...
        ]
    }

Metadata

All output formats will return a metadata dictionary, with the following fields:

{
    "table_of_contents": [
      {
        "title": "Introduction",
        "heading_level": 1,
        "page_id": 0,
        "polygon": [...]
      }
    ], // computed PDF table of contents
    "page_stats": [
      {
        "page_id":  0, 
        "text_extraction_method": "pdftext",
        "block_counts": [("Span", 200), ...]
      },
      ...
    ]
}

LLM Services

When running with the --use_llm flag, you have a choice of services you can use:

Gemini - this will use the Gemini developer API by default. You'll need to pass --gemini_api_key to configuration.
Google Vertex - this will use vertex, which can be more reliable. You'll need to pass --vertex_project_id. To use it, set --llm_service=marker.services.vertex.GoogleVertexService.
Ollama - this will use local models. You can configure --ollama_base_url and --ollama_model. To use it, set --llm_service=marker.services.ollama.OllamaService.
Claude - this will use the anthropic API. You can configure --claude_api_key, and --claude_model_name. To use it, set --llm_service=marker.services.claude.ClaudeService.
OpenAI - this supports any openai-like endpoint. You can configure --openai_api_key, --openai_model, and --openai_base_url. To use it, set --llm_service=marker.services.openai.OpenAIService.

These services may have additional optional configuration as well - you can see it by viewing the classes.

Internals

Marker is easy to extend. The core units of marker are:

Providers, at marker/providers. These provide information from a source file, like a PDF.
Builders, at marker/builders. These generate the initial document blocks and fill in text, using info from the providers.
Processors, at marker/processors. These process specific blocks, for example the table formatter is a processor.
Renderers, at marker/renderers. These use the blocks to render output.
Schema, at marker/schema. The classes for all the block types.
Converters, at marker/converters. They run the whole end to end pipeline.

To customize processing behavior, override the processors. To add new output formats, write a new renderer. For additional input formats, write a new provider.

Processors and renderers can be directly passed into the base PDFConverter, so you can specify your own custom processing easily.

API server

There is a very simple API server you can run like this:

pip install -U uvicorn fastapi python-multipart
marker_server --port 8001

This will start a fastapi server that you can access at localhost:8001. You can go to localhost:8001/docs to see the endpoint options.

You can send requests like this:

import requests
import json

post_data = {
    'filepath': 'FILEPATH',
    # Add other params here
}

requests.post("http://localhost:8001/marker", data=json.dumps(post_data)).json()

Note that this is not a very robust API, and is only intended for small-scale use. If you want to use this server, but want a more robust conversion option, you can use the hosted Datalab API.

Troubleshooting

There are some settings that you may find useful if things aren't working the way you expect:

If you have issues with accuracy, try setting --use_llm to use an LLM to improve quality. You must set GOOGLE_API_KEY to a Gemini API key for this to work.
Make sure to set force_ocr if you see garbled text - this will re-OCR the document.
TORCH_DEVICE - set this to force marker to use a given torch device for inference.
If you're getting out of memory errors, decrease worker count. You can also try splitting up long PDFs into multiple files.

Debugging

Pass the debug option to activate debug mode. This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information.

Benchmarks

Overall PDF Conversion

We created a benchmark set by extracting single PDF pages from common crawl. We scored based on a heuristic that aligns text with ground truth text segments, and an LLM as a judge scoring method.

Method	Avg Time	Heuristic Score	LLM Score
marker	2.83837	95.6709	4.23916
llamaparse	23.348	84.2442	3.97619
mathpix	6.36223	86.4281	4.15626
docling	3.69949	86.7073	3.70429

Benchmarks were run on an H100 for markjer and docling - llamaparse and mathpix used their cloud services. We can also look at it by document type:

Document Type	Marker heuristic	Marker LLM	Llamaparse Heuristic	Llamaparse LLM	Mathpix Heuristic	Mathpix LLM	Docling Heuristic	Docling LLM
Scientific paper	96.6737	4.34899	87.1651	3.96421	91.2267	4.46861	92.135	3.72422
Book page	97.1846	4.16168	90.9532	4.07186	93.8886	4.35329	90.0556	3.64671
Other	95.1632	4.25076	81.1385	4.01835	79.6231	4.00306	83.8223	3.76147
Form	88.0147	3.84663	66.3081	3.68712	64.7512	3.33129	68.3857	3.40491
Presentation	95.1562	4.13669	81.2261	4	83.6737	3.95683	84.8405	3.86331
Financial document	95.3697	4.39106	82.5812	4.16111	81.3115	4.05556	86.3882	3.8
Letter	98.4021	4.5	93.4477	4.28125	96.0383	4.45312	92.0952	4.09375
Engineering document	93.9244	4.04412	77.4854	3.72059	80.3319	3.88235	79.6807	3.42647
Legal document	96.689	4.27759	86.9769	3.87584	91.601	4.20805	87.8383	3.65552
Newspaper page	98.8733	4.25806	84.7492	3.90323	96.9963	4.45161	92.6496	3.51613
Magazine page	98.2145	4.38776	87.2902	3.97959	93.5934	4.16327	93.0892	4.02041

Throughput

We benchmarked throughput using a single long PDF.

Method	Time per page	Time per document	VRAM used
marker	0.18	43.42	3.17GB

The projected throughput is 122 pages per second on an H100 - we can run 22 individual processes given the VRAM used.

Table Conversion

Marker can extract tables from PDFs using marker.converters.table.TableConverter. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of FinTabNet. The HTML representations are compared using a tree edit distance based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves these scores:

Method	Avg score	Total tables
marker	0.816	99
marker w/use_llm	0.907	99
gemini	0.829	99

The --use_llm flag can significantly improve table recognition performance, as you can see.

We filter out tables that we cannot align with the ground truth, since fintabnet and our layout model have slightly different detection methods (this results in some tables being split/merged).

Running your own benchmarks

You can benchmark the performance of marker on your machine. Install marker manually with:

git clone https://github.com/VikParuchuri/marker.git
poetry install

Overall PDF Conversion

Download the benchmark data here and unzip. Then run the overall benchmark like this:

python benchmarks/overall.py --methods marker --scores heuristic,llm

Options:

--use_llm use an llm to improve the marker results.
--max_rows how many rows to process for the benchmark.
--methods can be llamaparse, mathpix, docling, marker. Comma separated.
--scores which scoring functions to use, can be llm, heuristic. Comma separated.

Table Conversion

The processed FinTabNet dataset is hosted here and is automatically downloaded. Run the benchmark with:

python benchmarks/table/table.py --max_rows 100

Options:

--use_llm uses an llm with marker to improve accuracy.
--use_gemini also benchmarks gemini 2.0 flash.

How it works

Marker is a pipeline of deep learning models:

It only uses models where necessary, which improves speed and accuracy.

Limitations

PDF is a tricky format, so marker will not always work perfectly. Here are some known limitations that are on the roadmap to address:

Very complex layouts, with nested tables and forms, may not work
Forms may not be rendered well

Note: Passing the --use_llm flag will mostly solve these issues.

Thanks

This work would not have been possible without amazing open source models and datasets, including (but not limited to):

Surya
Texify
Pypdfium2/pdfium
DocLayNet from IBM

Thank you to the authors of these models and datasets for making them available to the community!

License

Alex-Calvin/marker

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