Donut 🍩, 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 🐶, Synthetic Document Generator, that helps the model pre-training to be flexible on various 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. In ECCV 2022.
Gradio web demos are available!   |
|---|
 |
- You can run the demo with
./app.pyfile. - Sample images are available at
./miscand more receipt images are available at CORD dataset link. - Web demos are available from the links in the following table.
- Note: We have updated the Google Colab demo (as of June 15, 2023) to ensure its proper working.
| Task | Sec/Img | Score | Trained Model | Demo |
|---|---|---|---|---|
| CORD (Document Parsing) | 0.7 / 0.7 / 1.2 |
91.3 / 91.1 / 90.9 |
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 (updated at 23.06.15) |
| Train Ticket (Document Parsing) | 0.6 | 98.7 | donut-base-finetuned-zhtrainticket | google colab demo (updated at 23.06.15) |
| RVL-CDIP (Document Classification) | 0.75 | 95.3 | donut-base-finetuned-rvlcdip | gradio space web demo, google colab demo (updated at 23.06.15) |
| DocVQA Task1 (Document VQA) | 0.78 | 67.5 | donut-base-finetuned-docvqa | gradio space web demo, google colab demo (updated at 23.06.15) |
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.
2024-05-15 We have introduced the opportunity to retrieve the scores with the extracted items with the usual
DonutModel. The inference method has the return_confs to return the scores of the predicted items. The parameter
is set to True by default. If you don't want the scores, please set return_confs to False.
2023-06-15 We have updated all Google Colab demos to ensure its proper working.
2022-11-14 New version 1.0.9 is released (pip install donut-python --upgrade). See 1.0.9 Release Notes.
2022-08-12 Donut 🍩 is also available at huggingface/transformers 🤗 (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 🍩 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-pythonor 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-python == 1.0.1 with:
- torch == 1.11.0+cu113
- torchvision == 0.12.0+cu113
- pytorch-lightning == 1.6.4
- transformers == 4.11.3
- timm == 0.5.4
Note: From several reported issues, we have noticed increased challenges in configuring the testing environment for donut-python due to recent updates in key dependency libraries. While we are actively working on a solution, we have updated the Google Colab demo (as of June 15, 2023) to ensure its proper working. For assistance, we encourage you to refer to the following demo links: CORD Colab Demo, Train Ticket Colab Demo, RVL-CDIP Colab Demo, DocVQA Colab Demo.
This repository assumes the following structure of dataset:
> tree dataset_name
dataset_name
├── test
│ ├── metadata.jsonl
│ ├── {image_path0}
│ ├── {image_path1}
│ .
│ .
├── train
│ ├── metadata.jsonl
│ ├── {image_path0}
│ ├── {image_path1}
│ .
│ .
└── validation
├── metadata.jsonl
├── {image_path0}
├── {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.jsonlfile 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_parseorgt_parses. Other fields (metadata) can be added to the dictionary but will not be used.
donutinterprets all tasks as a JSON prediction problem. As a result, alldonutmodel training share a same pipeline. For training and inference, the only thing to do is preparinggt_parseorgt_parsesfor 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_parsesshould 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 🐶 to generate synthetic images for the text reading task with proper
gt_parse. See./synthdog/README.mdfor 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%|█████████████| 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 Hugging Face modelhub or local path.--dataset_name_or_paths: string format (json dumped), list of dataset names in Hugging Face 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%|█████████████| 100/100 [00:35<00:00, 2.80it/s]
Total number of samples: 100, Tree Edit Distance (TED) based accuracy score: 0.9129639764131697, F1 accuracy score: 0.8406020841373987Some important arguments:
--dataset_name_or_path: string format, the target dataset name in Hugging Face datasets or local path.--pretrained_model_name_or_path: string format, the model name in Hugging Face modelhub or local path.--save_path: file path to save predictions and scores.
If you find this work useful to you, please cite:
@inproceedings{kim2022donut,
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},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}MIT license
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