Donut π©, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). In addition, we present SynthDoG πΆ, Synthetic Document Generator, that helps the model pre-training to be flexible on vairous languages and domains.
Our academic paper, which describes our method in detail and provides full experimental results and analyses, can be found here:
OCR-free Document Understanding Transformer.
Geewook Kim, Teakgyu Hong, Moonbin Yim, JeongYeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park. To appear at ECCV 2022.
Gradio web demos are available! |
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- You can run the demo with
./app.py
file. - Sample images are available at
./misc
and more receipt images are available at CORD dataset link. - Web demos are available from the links in the following table.
Task | Sec/Img | Score | Trained Model | Demo |
---|---|---|---|---|
CORD (Document Parsing) | 0.7 / 0.7 / 1.2 |
93.7 / 93.5 / 93.5 |
donut-base-finetuned-cord-v2 (1280) / donut-base-finetuned-cord-v1 (1280) / donut-base-finetuned-cord-v1-2560 |
gradio space web demo, google colab demo |
Train Ticket (Document Parsing) | 0.6 | 98.8 | donut-base-finetuned-zhtrainticket | google colab demo |
RVL-CDIP (Document Classification) | 0.75 | 95.3 | donut-base-finetuned-rvlcdip | gradio space web demo, google colab demo |
DocVQA Task1 (Document VQA) | 0.78 | 67.5 | donut-base-finetuned-docvqa | gradio space web demo, google colab demo |
The links to the pre-trained backbones are here:
donut-base
: trained with 64 A100 GPUs (~2.5 days), number of layers (encoder: {2,2,14,2}, decoder: 4), input size 2560x1920, swin window size 10, IIT-CDIP (11M) and SynthDoG (English, Chinese, Japanese, Korean, 0.5M x 4).donut-proto
: (preliminary model) trained with 8 V100 GPUs (~5 days), number of layers (encoder: {2,2,18,2}, decoder: 4), input size 2048x1536, swin window size 8, and SynthDoG (English, Japanese, Korean, 0.4M x 3).
Please see our paper for more details.
The links to the SynthDoG-generated datasets are here:
synthdog-en
: English, 0.5M.synthdog-zh
: Chinese, 0.5M.synthdog-ja
: Japanese, 0.5M.synthdog-ko
: Korean, 0.5M.
To generate synthetic datasets with our SynthDoG, please see ./synthdog/README.md
and our paper for details.
2022-08-23 New version 1.0.7 is released (pip install donut-python --upgrade
). See 1.0.7 Release Notes.
2022-08-12 Donut π© is also available at huggingface/transformers π€ (contributed by @NielsRogge). donut-python
loads the pre-trained weights from the official
branch of the model repositories. See 1.0.5 Release Notes.
2022-08-05 A well-executed hands-on tutorial on donut π© is published at Towards Data Science (written by @estaudere).
2022-07-20 First Commit, We release our code, model weights, synthetic data and generator.
pip install donut-python
or clone this repository and install the dependencies:
git clone https://github.com/clovaai/donut.git
cd donut/
conda create -n donut_official python=3.7
conda activate donut_official
pip install .
We tested donut with:
- torch == 1.11.0+cu113
- torchvision == 0.12.0+cu113
- pytorch-lightning == 1.6.4
- transformers == 4.11.3
- timm == 0.5.4
This repository assumes the following structure of dataset:
> tree dataset_name
dataset_name
βββ test
β βββ metadata.jsonl
β βββ {image_path0}
β βββ {image_path1}
β .
β .
βββ train
β βββ metadata.jsonl
β βββ {image_path0}
β βββ {image_path1}
β .
β .
βββ validation
βββ metadata.jsonl
βββ {image_path0}
βββ {image_path1}
.
.
> cat dataset_name/test/metadata.jsonl
{"file_name": {image_path0}, "ground_truth": "{\"gt_parse\": {ground_truth_parse}, ... {other_metadata_not_used} ... }"}
{"file_name": {image_path1}, "ground_truth": "{\"gt_parse\": {ground_truth_parse}, ... {other_metadata_not_used} ... }"}
.
.
- The structure of
metadata.jsonl
file is in JSON Lines text format, i.e.,.jsonl
. Each line consists offile_name
: relative path to the image file.ground_truth
: string format (json dumped), the dictionary contains eithergt_parse
orgt_parses
. Other fields (metadata) can be added to the dictionary but will not be used.
donut
interprets all tasks as a JSON prediction problem. As a result, alldonut
model training share a same pipeline. For training and inference, the only thing to do is preparinggt_parse
orgt_parses
for the task in format described below.
The gt_parse
follows the format of {"class" : {class_name}}
, for example, {"class" : "scientific_report"}
or {"class" : "presentation"}
.
The gt_parse
is a JSON object that contains full information of the document image, for example, the JSON object for a receipt may look like {"menu" : [{"nm": "ICE BLACKCOFFEE", "cnt": "2", ...}, ...], ...}
.
- More examples are available at CORD dataset.
- Google colab demo is available here.
- Gradio web demo is available here.
The gt_parses
follows the format of [{"question" : {question_sentence}, "answer" : {answer_candidate_1}}, {"question" : {question_sentence}, "answer" : {answer_candidate_2}}, ...]
, for example, [{"question" : "what is the model name?", "answer" : "donut"}, {"question" : "what is the model name?", "answer" : "document understanding transformer"}]
.
- DocVQA Task1 has multiple answers, hence
gt_parses
should be a list of dictionary that contains a pair of question and answer. - Google colab demo is available here.
- Gradio web demo is available here.
The gt_parse
looks like {"text_sequence" : "word1 word2 word3 ... "}
- This task is also a pre-training task of Donut model.
- You can use our SynthDoG πΆ to generate synthetic images for the text reading task with proper
gt_parse
. See./synthdog/README.md
for details.
This is the configuration of Donut model training on CORD dataset used in our experiment. We ran this with a single NVIDIA A100 GPU.
python train.py --config config/train_cord.yaml \
--pretrained_model_name_or_path "naver-clova-ix/donut-base" \
--dataset_name_or_paths '["naver-clova-ix/cord-v2"]' \
--exp_version "test_experiment"
.
.
Prediction: <s_menu><s_nm>Lemon Tea (L)</s_nm><s_cnt>1</s_cnt><s_price>25.000</s_price></s_menu><s_total><s_total_price>25.000</s_total_price><s_cashprice>30.000</s_cashprice><s_changeprice>5.000</s_changeprice></s_total>
Answer: <s_menu><s_nm>Lemon Tea (L)</s_nm><s_cnt>1</s_cnt><s_price>25.000</s_price></s_menu><s_total><s_total_price>25.000</s_total_price><s_cashprice>30.000</s_cashprice><s_changeprice>5.000</s_changeprice></s_total>
Normed ED: 0.0
Prediction: <s_menu><s_nm>Hulk Topper Package</s_nm><s_cnt>1</s_cnt><s_price>100.000</s_price></s_menu><s_total><s_total_price>100.000</s_total_price><s_cashprice>100.000</s_cashprice><s_changeprice>0</s_changeprice></s_total>
Answer: <s_menu><s_nm>Hulk Topper Package</s_nm><s_cnt>1</s_cnt><s_price>100.000</s_price></s_menu><s_total><s_total_price>100.000</s_total_price><s_cashprice>100.000</s_cashprice><s_changeprice>0</s_changeprice></s_total>
Normed ED: 0.0
Prediction: <s_menu><s_nm>Giant Squid</s_nm><s_cnt>x 1</s_cnt><s_price>Rp. 39.000</s_price><s_sub><s_nm>C.Finishing - Cut</s_nm><s_price>Rp. 0</s_price><sep/><s_nm>B.Spicy Level - Extreme Hot Rp. 0</s_price></s_sub><sep/><s_nm>A.Flavour - Salt & Pepper</s_nm><s_price>Rp. 0</s_price></s_sub></s_menu><s_sub_total><s_subtotal_price>Rp. 39.000</s_subtotal_price></s_sub_total><s_total><s_total_price>Rp. 39.000</s_total_price><s_cashprice>Rp. 50.000</s_cashprice><s_changeprice>Rp. 11.000</s_changeprice></s_total>
Answer: <s_menu><s_nm>Giant Squid</s_nm><s_cnt>x1</s_cnt><s_price>Rp. 39.000</s_price><s_sub><s_nm>C.Finishing - Cut</s_nm><s_price>Rp. 0</s_price><sep/><s_nm>B.Spicy Level - Extreme Hot</s_nm><s_price>Rp. 0</s_price><sep/><s_nm>A.Flavour- Salt & Pepper</s_nm><s_price>Rp. 0</s_price></s_sub></s_menu><s_sub_total><s_subtotal_price>Rp. 39.000</s_subtotal_price></s_sub_total><s_total><s_total_price>Rp. 39.000</s_total_price><s_cashprice>Rp. 50.000</s_cashprice><s_changeprice>Rp. 11.000</s_changeprice></s_total>
Normed ED: 0.039603960396039604
Epoch 29: 100%|βββββββββββββ| 200/200 [01:49<00:00, 1.82it/s, loss=0.00327, exp_name=train_cord, exp_version=test_experiment]
Some important arguments:
--config
: config file path for model training.--pretrained_model_name_or_path
: string format, model name in huggingface modelhub or local path.--dataset_name_or_paths
: string format (json dumped), list of dataset names in huggingface datasets or local paths.--result_path
: file path to save model outputs/artifacts.--exp_version
: used for experiment versioning. The output files are saved at{result_path}/{exp_version}/*
With the trained model, test images and ground truth parses, you can get inference results and accuracy scores.
python test.py --dataset_name_or_path naver-clova-ix/cord-v2 --pretrained_model_name_or_path ./result/train_cord/test_experiment --save_path ./result/output.json
100%|βββββββββββββ| 100/100 [00:35<00:00, 2.80it/s]
Total number of samples: 100, Tree Edit Distance (TED) based accuracy score: 0.9374973163596032, F1 accuracy score: 0.8406020841373987
Some important arguments:
--dataset_name_or_path
: string format, the target dataset name in huggingface datasets or local path.--pretrained_model_name_or_path
: string format, the model name in huggingface modelhub or local path.--save_path
: file path to save predictions and scores.
If you find this work useful to you, please cite:
@article{kim2021donut,
title={OCR-free Document Understanding Transformer},
author={Kim, Geewook and Hong, Teakgyu and Yim, Moonbin and Nam, JeongYeon and Park, Jinyoung and Yim, Jinyeong and Hwang, Wonseok and Yun, Sangdoo and Han, Dongyoon and Park, Seunghyun},
journal={arXiv preprint arXiv:2111.15664},
year={2021}
}
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