Marker

Marker converts PDF to markdown quickly and accurately.

Supports a wide range of documents (optimized for books and scientific papers)
Supports all languages
Removes headers/footers/other artifacts
Formats tables and code blocks
Extracts and saves images along with the markdown
Converts most equations to latex
Works on GPU, CPU, or MPS

How it works

Marker is a pipeline of deep learning models:

Extract text, OCR if necessary (heuristics, surya, tesseract)
Detect page layout and find reading order (surya)
Clean and format each block (heuristics, texify
Combine blocks and postprocess complete text (heuristics, pdf_postprocessor)

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

Examples

PDF	Type	Marker	Nougat
Think Python	Textbook	View	View
Think OS	Textbook	View	View
Switch Transformers	arXiv paper	View	View
Multi-column CNN	arXiv paper	View	View

Performance

The above results are with marker and nougat setup so they each take ~4GB of VRAM on an A6000.

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

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 (.25s/page for 50 page doc)

Community

Discord is where we discuss future development.

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:

Marker will not convert 100% of equations to LaTeX. This is because it has to detect then convert.
Tables are not always formatted 100% correctly - text can be in the wrong column.
Whitespace and indentations are not always respected.
Not all lines/spans will be joined properly.
This works best on digital PDFs that won't require a lot of OCR. It's optimized for speed, and limited OCR is used to fix errors.

Installation

You'll need python 3.9+ 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

Optional: OCRMyPDF

Only needed if you want to use the optional ocrmypdf as the ocr backend. Note that ocrmypdf includes Ghostscript, an AGPL dependency, but calls it via CLI, so it does not trigger the license provisions.

See the instructions here

Usage

First, some configuration:

Inspect the settings in marker/settings.py. You can override any settings with environment variables.
Your torch device will be automatically detected, but you can override this. For example, TORCH_DEVICE=cuda.
By default, marker will use surya for OCR. Surya is slower on CPU, but more accurate than tesseract. It also doesn't require you to specify the languages in the document. If you want faster OCR, set OCR_ENGINE to ocrmypdf. This also requires external dependencies (see above). If you don't want OCR at all, set OCR_ENGINE to None.
Some PDFs, even digital ones, have bad text in them. Set OCR_ALL_PAGES=true to force OCR if you find bad output from marker.

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 /path/to/output/folder --batch_multiplier 2 --max_pages 10

--batch_multiplier is how much to multiply default batch sizes by if you have extra VRAM. Higher numbers will take more VRAM, but process faster. Set to 2 by default. The default batch sizes will take ~3GB of VRAM.
--max_pages is the maximum number of pages to process. Omit this to convert the entire document.
--start_page is the page to start from (default is None, will start from the first page).
--langs is an optional comma separated list of the languages in the document, for OCR. Optional by default, required if you use tesseract.

The list of supported languages for surya OCR is here. If you need more languages, you can use any language supported by Tesseract if you set OCR_ENGINE to ocrmypdf. If you don't need OCR, marker can work with any language.

Convert multiple files

marker /path/to/input/folder /path/to/output/folder --workers 4 --max 10

--workers is the number of pdfs to convert at once. This is set to 1 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.
--max is the maximum number of pdfs to convert. Omit this to convert all pdfs in the folder.
--min_length is the minimum number of characters that need to be extracted from a pdf before it will be considered for processing. If you're processing a lot of pdfs, I recommend setting this to avoid OCRing pdfs that are mostly images. (slows everything down)
--metadata_file is an optional path to a json file with metadata about the pdfs. If you provide it, it will be used to set the language for each pdf. Setting language is optional for surya (default), but required for tesseract. The format is:

{
  "pdf1.pdf": {"languages": ["English"]},
  "pdf2.pdf": {"languages": ["Spanish", "Russian"]},
  ...
}

You can use language names or codes. The exact codes depend on the OCR engine. See here for a full list for surya codes, and here for tesseract.

Convert multiple files on multiple GPUs

METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out

METADATA_FILE is an optional path to a json file with metadata about the pdfs. See above for the format.
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.
MIN_LENGTH is the minimum number of characters that need to be extracted from a pdf before it will be considered for processing. If you're processing a lot of pdfs, I recommend setting this to avoid OCRing pdfs that are mostly images. (slows everything down)

Note that the env variables above are specific to this script, and cannot be set in local.env.

Use from python

See the convert_single_pdf function for additional arguments that can be passed.

from marker.convert import convert_single_pdf
from marker.models import load_all_models

fpath = "FILEPATH"
model_lst = load_all_models()
full_text, images, out_meta = convert_single_pdf(fpath, model_lst)

Output format

The output will be a markdown file, but there will also be a metadata json file that gives information about the conversion process. It has these fields:

{
    "languages": null, // any languages that were passed in
    "filetype": "pdf", // type of the file
    "pdf_toc": [], // the table of contents from the pdf
    "computed_toc": [], //the computed table of contents
    "pages": 10, // page count
    "ocr_stats": {
        "ocr_pages": 0, // number of pages OCRed
        "ocr_failed": 0, // number of pages where OCR failed
        "ocr_success": 0,
        "ocr_engine": "none"
    },
    "block_stats": {
        "header_footer": 0,
        "code": 0, // number of code blocks
        "table": 2, // number of tables
        "equations": {
            "successful_ocr": 0,
            "unsuccessful_ocr": 0,
            "equations": 0
        }
    }
}

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.

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 run against the hosted Datalab API. You'll need to register and get an API key, then run:

marker_server --port 8001 --api_key API_KEY

Note: This is not the recommended way to use the Datalab API - it's only provided as a convenience for people wrapping the marker repo. The recommended way is to make a post request to the endpoint directly from your code vs proxying through this server.

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()

Troubleshooting

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

OCR_ALL_PAGES - set this to true to force OCR all pages. This can be very useful if there is garbled text in the output of marker.
TORCH_DEVICE - set this to force marker to use a given torch device for inference.
OCR_ENGINE - can set this to surya or ocrmypdf.
Verify that you set the languages correctly, or passed in a metadata file.
If you're getting out of memory errors, decrease worker count (increased the VRAM_PER_TASK setting). You can also try splitting up long PDFs into multiple files.

In general, if output is not what you expect, trying to OCR the PDF is a good first step. Not all PDFs have good text/bboxes embedded in them.

Debugging

Set DEBUG=true to save data to the debug subfolder in the marker root directory. This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information.

Useful settings

These settings can improve/change output quality:

OCR_ALL_PAGES will force OCR across the document. Many PDFs have bad text embedded due to older OCR engines being used.
PAGINATE_OUTPUT will put a horizontal rule between pages. Default: False. The horizontal rule will be \n\n, then {PAGE_NUMBER}, then 48 single dashes -, then \n\n. The separator can be configured via the PAGE_SEPARATOR setting.
EXTRACT_IMAGES will extract images and save separately. Default: True.
BAD_SPAN_TYPES specifies layout blocks to remove from the markdown output.

Benchmarks

Benchmarking PDF extraction quality is hard. I've created a test set by finding books and scientific papers that have a pdf version and a latex source. I convert the latex to text, and compare the reference to the output of text extraction methods. It's noisy, but at least directionally correct.

Benchmarks show that marker is 4x faster than nougat, and more accurate outside arXiv (nougat was trained on arXiv data). We show naive text extraction (pulling text out of the pdf with no processing) for comparison.

Speed

Method	Average Score	Time per page	Time per document
marker	0.613721	0.631991	58.1432
nougat	0.406603	2.59702	238.926

Accuracy

First 3 are non-arXiv books, last 3 are arXiv papers.

Method	multicolcnn.pdf	switch_trans.pdf	thinkpython.pdf	thinkos.pdf	thinkdsp.pdf	crowd.pdf
marker	0.536176	0.516833	0.70515	0.710657	0.690042	0.523467
nougat	0.44009	0.588973	0.322706	0.401342	0.160842	0.525663

Peak GPU memory usage during the benchmark is 4.2GB for nougat, and 4.1GB for marker. Benchmarks were run on an A6000 Ada.

Throughput

Marker takes about 4GB of VRAM on average per task, so you can convert 12 documents in parallel on an A6000.

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

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

python benchmarks/overall.py data/pdfs data/references report.json --nougat

This will benchmark marker against other text extraction methods. It sets up batch sizes for nougat and marker to use a similar amount of GPU RAM for each.

Omit --nougat to exclude nougat from the benchmark. I don't recommend running nougat on CPU, since it is very slow.

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!