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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
| Date | Stars |
|---|---|
| 2026-07-24 | 6908 |
| 2026-07-25 | 6908 |
| 2026-07-28 | 6911 |
| 2026-07-30 | 6911 |
| 2026-08-06 | 6911 |
Today
— stars today
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Momentum
0.0
growth rate 0.00%/day
<div align="center">
# Donut 🍩 : Document Understanding Transformer
[](https://arxiv.org/abs/2111.15664)
[](#how-to-cite)
[](#demo)
[](#demo)
[](https://pypi.org/project/donut-python)
[](https://pepy.tech/project/donut-python)
Official Implementation of Donut and SynthDoG | [Paper](https://arxiv.org/abs/2111.15664) | [Slide](https://docs.google.com/presentation/d/1gv3A7t4xpwwNdpxV_yeHzEOMy-exJCAz6AlAI9O5fS8/edit?usp=sharing) | [Poster](https://docs.google.com/presentation/d/1m1f8BbAm5vxPcqynn_MbFfmQAlHQIR5G72-hQUFS2sk/edit?usp=sharing)
</div>
## Introduction
**Donut** 🍩, **Do**cume**n**t **u**nderstanding **t**ransformer, 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** 🐶, **Synth**etic **Do**cument **G**enerator, 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:<br>
> [**OCR-free Document Understanding Transformer**](https://arxiv.org/abs/2111.15664).<br>
> [Geewook Kim](https://geewook.kim), [Teakgyu Hong](https://dblp.org/pid/183/0952.html), [Moonbin Yim](https://github.com/moonbings), [JeongYeon Nam](https://github.com/long8v), [Jinyoung Park](https://github.com/jyp1111), [Jinyeong Yim](https://jinyeong.github.io), [Wonseok Hwang](https://scholar.google.com/citations?user=M13_WdcAAAAJ), [Sangdoo Yun](https://sangdooyun.github.io), [Dongyoon Han](https://dongyoonhan.github.io), [Seunghyun Park](https://scholar.google.com/citations?user=iowjmTwAAAAJ). In ECCV 2022.
<img width="946" alt="image" src="misc/overview.png">
## Pre-trained Models and Web Demos
Gradio web demos are available! [](#demo) [](#demo)
|:--:|
||
- 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](https://huggingface.co/datasets/naver-clova-ix/cord-v2).
- 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|<div id="demo">Demo</div>|
|---|---|---|---|---|
| [CORD](https://github.com/clovaai/cord) (Document Parsing) | 0.7 /<br> 0.7 /<br> 1.2 | 91.3 /<br> 91.1 /<br> 90.9 | [donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2/tree/official) (1280) /<br> [donut-base-finetuned-cord-v1](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v1/tree/official) (1280) /<br> [donut-base-finetuned-cord-v1-2560](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v1-2560/tree/official) | [gradio space web demo](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2),<br>[google colab demo (updated at 23.06.15)](https://colab.research.google.com/drive/1NMSqoIZ_l39wyRD7yVjw2FIuU2aglzJi?usp=sharing) |
| [Train Ticket](https://github.com/beacandler/EATEN) (Document Parsing) | 0.6 | 98.7 | [doExcerpt of 18,508 characters
Read on GitHub
Geewook Kim · NAVER Cloud AI & KAIST AI
50
3
Sam_S · NYU, TII
1
Shinji Yamada · Japan
1
Ikko Eltociear Ashimine · Japan
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1e8fbcec453e7be1, topic:ocr, readme:document parsing, desc:ocr
matched fp:1e8fbcec453e7be1, topic:computer-vision
matched fp:1e8fbcec453e7be1, topic:nlp, readme:information extraction