Marker converts PDF, EPUB, and MOBI to markdown. It's 10x faster than nougat, more accurate on most documents, and has low hallucination risk.
- Support for a range of PDF documents (optimized for books and scientific papers)
- Removes headers/footers/other artifacts
- Converts most equations to latex
- Formats code blocks and tables
- Support for multiple languages (although most testing is done in English). See
settings.py
for a language list. - Works on GPU, CPU, or MPS
Marker is a pipeline of deep learning models:
- Extract text, OCR if necessary (heuristics, tesseract)
- Detect page layout (layout segmenter, column detector)
- Clean and format each block (heuristics, nougat)
- Combine blocks and postprocess complete text (heuristics, pdf_postprocessor)
Relying on autoregressive forward passes to generate text is slow and prone to hallucination/repetition. From the nougat paper: We observed [repetition] in 1.5% of pages in the test set, but the frequency increases for out-of-domain documents.
In my anecdotal testing, repetitions happen on 5%+ of out-of-domain (non-arXiv) pages.
Nougat is an amazing model, but I wanted a faster and more general purpose solution. Marker is 10x faster and has low hallucination risk because it only passes equation blocks through an LLM forward pass.
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 |
The above results are with marker and nougat setup so they each take ~3GB of VRAM on an A6000.
See below for detailed speed and accuracy benchmarks, and instructions on how to run your own benchmarks.
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 convert fewer equations to latex than nougat. This is because it has to first detect equations, then convert them without hallucation.
- Whitespace and indentations are not always respected.
- Not all lines/spans will be joined properly.
- Only languages similar to English (Spanish, French, German, Russian, etc) are supported. Languages with different character sets (Chinese, Japanese, Korean, etc) are not.
- 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.
This has been tested on Mac and Linux (Ubuntu and Debian). You'll need python 3.9+ and poetry.
First, clone the repo:
git clone https://github.com/VikParuchuri/marker.git
cd marker
- Install system requirements
- Optional: Install tesseract 5 by following these instructions or running
scripts/install/tesseract_5_install.sh
. - Install ghostscript > 9.55 by following these instructions or running
scripts/install/ghostscript_install.sh
. - Install other requirements with
cat scripts/install/apt-requirements.txt | xargs sudo apt-get install -y
- Optional: Install tesseract 5 by following these instructions or running
- Set the tesseract data folder path
- Find the tesseract data folder
tessdata
withfind / -name tessdata
. Make sure to use the one corresponding to the latest tesseract version if you have multiple. - Create a
local.env
file in the rootmarker
folder withTESSDATA_PREFIX=/path/to/tessdata
inside it
- Find the tesseract data folder
- Install python requirements
poetry install
poetry shell
to activate your poetry venv
- Update pytorch since poetry doesn't play nicely with it
- GPU only: run
pip install torch
to install other torch dependencies. - CPU only: Uninstall torch, then follow the CPU install instructions.
- GPU only: run
- Install system requirements from
scripts/install/brew-requirements.txt
- Set the tesseract data folder path
- Find the tesseract data folder
tessdata
withbrew list tesseract
- Create a
local.env
file in the rootmarker
folder withTESSDATA_PREFIX=/path/to/tessdata
inside it
- Find the tesseract data folder
- Install python requirements
poetry install
poetry shell
to activate your poetry venv
First, some configuration:
- Set your torch device in the
local.env
file. For example,TORCH_DEVICE=cuda
orTORCH_DEVICE=mps
.cpu
is the default.- If using GPU, set
INFERENCE_RAM
to your GPU VRAM (per GPU). For example, if you have 16 GB of VRAM, setINFERENCE_RAM=16
. - Depending on your document types, marker's average memory usage per task can vary slightly. You can configure
VRAM_PER_TASK
to adjust this if you notice tasks failing with GPU out of memory errors.
- If using GPU, set
- Inspect the other settings in
marker/settings.py
. You can override any settings in thelocal.env
file, or by setting environment variables.- By default, the final editor model is off. Turn it on with
ENABLE_EDITOR_MODEL
. - By default, marker will use ocrmypdf for OCR, which is slower than base tesseract, but higher quality. You can change this with the
OCR_ENGINE
setting.
- By default, the final editor model is off. Turn it on with
Run convert_single.py
, like this:
python convert_single.py /path/to/file.pdf /path/to/output.md --parallel_factor 2 --max_pages 10
--parallel_factor
is how much to increase batch size and parallel OCR workers by. Higher numbers will take more VRAM and CPU, but process faster. Set to 1 by default.--max_pages
is the maximum number of pages to process. Omit this to convert the entire document.
Make sure the DEFAULT_LANG
setting is set appropriately for your document.
Run convert.py
, like this:
python convert.py /path/to/input/folder /path/to/output/folder --workers 10 --max 10 --metadata_file /path/to/metadata.json --min_length 10000
--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. Parallelism will not increase beyondINFERENCE_RAM / VRAM_PER_TASK
if you're using GPU.--max
is the maximum number of pdfs to convert. Omit this to convert all pdfs in the folder.--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. If not,DEFAULT_LANG
will be used. The format is:--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)
{
"pdf1.pdf": {"language": "English"},
"pdf2.pdf": {"language": "Spanish"},
...
}
Run chunk_convert.sh
, like this:
MIN_LENGTH=10000 METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 bash chunk_convert.sh ../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 be2
or greater.NUM_WORKERS
is the number of parallel processes to run on each GPU. Per-GPU parallelism will not increase beyondINFERENCE_RAM / VRAM_PER_TASK
.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)
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.
Benchmarks show that marker is 10x 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 |
---|---|---|---|
naive | 0.350727 | 0.00152378 | 0.326524 |
marker | 0.641062 | 0.360622 | 77.2762 |
nougat | 0.629211 | 3.77259 | 808.413 |
Accuracy
First 3 are non-arXiv books, last 3 are arXiv papers.
Method | switch_trans.pdf | crowd.pdf | multicolcnn.pdf | thinkos.pdf | thinkdsp.pdf | thinkpython.pdf |
---|---|---|---|---|---|---|
naive | 0.244114 | 0.140669 | 0.0868221 | 0.366856 | 0.412521 | 0.468281 |
marker | 0.482091 | 0.466882 | 0.537062 | 0.754347 | 0.78825 | 0.779536 |
nougat | 0.696458 | 0.552337 | 0.735099 | 0.655002 | 0.645704 | 0.650282 |
Peak GPU memory usage during the benchmark is 3.3GB
for nougat, and 3.1GB
for marker. Benchmarks were run on an A6000.
Throughput
Marker takes about 2GB of VRAM on average per task, so you can convert 24 documents in parallel on an A6000.
You can benchmark the performance of marker on your machine. First, download the benchmark data here and unzip.
Then run benchmark.py
like this:
python benchmark.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.
Due to the licensing of the underlying models like layoutlmv3 and nougat, this is only suitable for noncommercial usage.
I'm building a version that can be used commercially, by stripping out the dependencies below. If you would like to get early access, email me at [email protected].
Here are the non-commercial/restrictive dependencies:
Other dependencies/datasets are openly licensed (doclaynet, byt5), or used in a way that is compatible with commercial usage (ghostscript).
This work would not have been possible without amazing open source models and datasets, including (but not limited to):
- Nougat from Meta
- Layoutlmv3 from Microsoft
- DocLayNet from IBM
- ByT5 from Google
Thank you to the authors of these models and datasets for making them available to the community!