This is the official repository for Nougat, the academic document PDF parser that understands LaTeX math and tables.

Project page: https://facebookresearch.github.io/nougat/

From pip:

pip install nougat-ocr

From repository:

pip install git+https://github.com/facebookresearch/nougat

Note, on Windows: If you want to utilize a GPU, make sure you first install the correct PyTorch version. Follow instructions here

There are extra dependencies if you want to call the model from an API or generate a dataset. Install via

pip install "nougat-ocr[api]" or pip install "nougat-ocr[dataset]"

To get predictions for a PDF run

$ nougat path/to/file.pdf -o output_directory

usage: nougat [-h] [--batchsize BATCHSIZE] [--checkpoint CHECKPOINT] [--out OUT] [--recompute] [--markdown] pdf [pdf ...]

positional arguments:
  pdf                   PDF(s) to process.

options:
  -h, --help            show this help message and exit
  --batchsize BATCHSIZE, -b BATCHSIZE
                        Batch size to use.
  --checkpoint CHECKPOINT, -c CHECKPOINT
                        Path to checkpoint directory.
  --out OUT, -o OUT     Output directory.
  --recompute           Recompute already computed PDF, discarding previous predictions.
  --markdown            Add postprocessing step for markdown compatibility.

In the output directory every PDF will be saved as a .mmd file, the lightweight markup language, mostly compatible with Mathpix Markdown (we make use of the LaTeX tables).

With the extra dependencies you use app.py to start an API. Call

$ nougat_api

To get a prediction of a PDF file by making a POST request to https://127.0.0.1:8503/predict/. It also accepts parameters start and stop to limit the computation to select page numbers (boundaries are included).

The response is a string with the markdown text of the document.

curl -X 'POST' \
  'https://127.0.0.1:8503/predict/' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@<PDFFILE.pdf>;type=application/pdf'

To use the limit the conversion to pages 1 to 5, ue the start/stop parameters in the request URL: https://127.0.0.1:8503/predict/?start=1&stop=5

To generate a dataset you need

1. A directory containing the PDFs
2. A directory containing the .html files (processed .tex files by LaTeXML) with the same folder structure
3. A binary file of pdffigures2 and a corresponding environment variable export PDFFIGURES_PATH="/path/to/binary.jar"

Next run

python -m nougat.dataset.split_htmls_to_pages --html path/html/root --pdfs path/pdf/root --out path/paired/output --figure path/pdffigures/outputs

Additional arguments include

Finally create a jsonl file that contains all the image paths, markdown text and meta information.

python -m nougat.dataset.create_index --dir path/paired/output --out index.jsonl

For each jsonl file you also need to generate a seek map for faster data loading:

python -m nougat.dataset.gen_seek file.jsonl

The resulting directory structure can look as follows:

root/
├── images
├── train.jsonl
├── train.seek.map
├── test.jsonl
├── test.seek.map
├── validation.jsonl
└── validation.seek.map

Note that the .mmd and .json files in the path/paired/output (here images) are no longer required. This can be useful for pushing to a S3 bucket by halving the amount of files.

To train or fine tune a Nougat model, run

python train.py --config config/train_nougat.yaml

Run

python test.py --checkpoint path/to/checkpoint --dataset path/to/test.jsonl --save_path path/to/results.json

To get the results for the different text modalities, run

python -m nougat.metrics path/to/results.json

@misc{blecher2023nougat,
      title={Nougat: Neural Optical Understanding for Academic Documents}, 
      author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic},
      year={2023},
      eprint={2308.13418},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

This repository builds on top of the Donut repository.

Nougat codebase is licensed under MIT.

Nougat model weights are licensed under CC-BY-NC.