Nougat: Neural Optical Understanding for Academic Documents

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/

Install

From pip:

pip install nougat-ocr

From repository:

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

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]"

Get prediction for a PDF

CLI

To get predictions for a PDF run

$ nougat path/to/file.pdf

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

positional arguments:
  pdf                   PDF(s) to process.

optional arguments:
  -h, --help            show this help message and exit
  --batchsize BATCHSIZE, -b BATCHSIZE
                        Batch size to use. Defaults to 6 which runs on 24GB VRAM.
  --checkpoint CHECKPOINT, -c CHECKPOINT
                        Path to checkpoint directory
  --out OUT, -o OUT     Output directory.

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).

API

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 http://127.0.0.1:8503/predict/. It also accepts parameters start and stop to limit the computation to select page numbers (boundaries are included).

Dataset

Generate dataset

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

Argument	Description
--recompute	recompute all splits
--markdown MARKDOWN	Markdown output dir
--workers WORKERS	How many processes to use
--dpi DPI	What resoultion the pages will be saved at
--timeout TIMEOUT	max time per paper in seconds
--tesseract	Tesseract OCR prediction for each page

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.

Training

To train or fine tune a Nougat model, run

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

Evaluation

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

Citation

@misc{blecher_nougat_2023,
      doi = {10.48550/ARXIV.2303.xxxxx},
      url = {https://arxiv.org/abs/2303.xxxxx},
      author = {Blecher, Lukas and Cucurull, Guillem and Scialom, Thomas and Stojnic, Robert},
      title = {Nougat: Neural Optical Understanding for Academic Documents},
      publisher = {arXiv},
      year = {2023},
      copyright = {arXiv.org perpetual, non-exclusive license}
}

Acknowledgments

This repository builds on top of the Donut repository.

License

Nougat is licensed under CC-BY-NC.