Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/hikopensource/DAVAR-Lab-OCR
OCR toolbox from Davar-Lab
https://github.com/hikopensource/DAVAR-Lab-OCR
dar ocr
Last synced: about 1 month ago
JSON representation
OCR toolbox from Davar-Lab
- Host: GitHub
- URL: https://github.com/hikopensource/DAVAR-Lab-OCR
- Owner: hikopensource
- License: apache-2.0
- Created: 2020-12-30T08:14:15.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-11-16T07:54:13.000Z (about 1 year ago)
- Last Synced: 2024-11-03T10:32:22.311Z (about 2 months ago)
- Topics: dar, ocr
- Language: Python
- Homepage:
- Size: 113 MB
- Stars: 737
- Watchers: 27
- Forks: 157
- Open Issues: 78
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
# DAVAR-OCR
This is the opensourced OCR repository of [DAVAR Lab](https://davar-lab.github.io/), from Hikvision Research Institute, China.
We begin to maintain this code repository to release the implementations of our recent academic publishments and some re-implementations of previous popular algorithms/modules in OCR.
We also provide some of the ablation experiment comparasions for better reproduction.
A short paper introduces DavarOCR is available at [arxiv](https://arxiv.org/pdf/2207.06695.pdf).
> Note: Due to the policy limits of the company. All of the codes were re-implemented based on the open-source frameworks, [mmdetection-2.11.0](https://github.com/open-mmlab/mmdetection/releases/tag/v2.11.0) and [mmcv-1.3.4](https://github.com/open-mmlab/mmcv/releases/tag/v1.3.4), from [open-mmlab](https://github.com/open-mmlab "open-mmlab"). The code architecture also refers to [mmocr](https://github.com/open-mmlab/mmocr), which means these two frameworks can be well compatible to each other.
## Implementations
To date, davarocr contains the following algorithms:
*Basic OCR Tasks*
***Text Detection***
- [x] [EAST](demo/text_detection/east) (CVPR 2017)
- [x] [MASK RCNN](demo/text_detection/mask_rcnn_det) (ICCV 2017)
- [x] [Text Perceptron Det](demo/text_detection/text_perceptron_det) (AAAI 2020)
***Text Recognition***
- [x] [Attention](demo/text_recognition/__base__) (CVPR 2016)
- [x] [CRNN](demo/text_recognition/__base__) (TPAMI 2017)
- [x] [ACE](demo/text_recognition/ace) (CVPR 2019)
- [x] [SPIN](demo/text_recognition/spin) (AAAI 2021)
- [x] [RF-Learning](demo/text_recognition/rflearning) (ICDAR 2021)
***Text Spotting***
- [x] [Mask RCNN E2E](demo/text_spotting/mask_rcnn_spot/)
- [x] [Text Perceptron E2E](demo/text_spotting/text_perceptron_spot/) (AAAI 2020)
- [x] [MANGO](demo/text_spotting/mango) (AAAI 2021)
- [x] [DLD](demo/text_spotting/dld) (ECCV 2022)
***Video Text Spotting***
- [x] [YORO](demo/videotext/yoro) (ACM MM 2019)
*Document Understanding Tasks*
***Information Extraction***
- [x] [Chargrid](demo/text_ie/chargrid) (EMNLP 2018)
- [x] [TRIE](demo/text_ie/trie) (ACM MM 2020)
***Table Recognition***
- [x] [LGPMA](demo/table_recognition/lgpma) (ICDAR 2021)
***Table Understanding***
- [x] [CTUNet](demo/table_understanding/ctunet) (ACMMM 2022)
***Layout Recognition***
- [x] [VSR](demo/text_layout/VSR) (ICDAR 2021)
***Reading Order Detection***
- [x] [GCN-PN](demo/reading_order_detection/GCN-PN) (ECCV 2020)
***Named Entity Reocognition***
- [x] [Bert-based NER](demo/ner/BERT), including BERT+CRF/Span/Softmax
- [x] [BiLSTM+CRF NER](demo/ner/bilstm_crf) (Arxiv 2016)
## Development Environment
The recommended environment requirements can be found in [mmdetection](https://github.com/open-mmlab/mmdetection/). Follows are the lowest compatible environment.
| Basic Env | version |
| :---------- | ------- |
| Python | 3.6+ |
| cuda | 10.0+ |
| cudnn | 7.6.3+ |
| pytorch | 1.3.0+ |
| torchvision | 0.4.1+ |
| opencv | 3.0.0+ |
> For some of the algorithms (EAST, Text Perceptron), C++ version [opencv](https://opencv.org/) are required. If you do not need to use these algorithms, you could temporarily ignore the error about 'opencv.hpp' or remove the related codes temporarily.
>
## Installation and Development Instruction
To Download the repository and install the davarocr, please follow the instructions:
```shell
git clone https://github.com/hikopensource/DAVAR-Lab-OCR.git
cd DAVAR-Lab-OCR/
bash setup.sh
```
This script will automatically download and install the "mmdetection" and "mmcv-full". You can also manually install them followinging the [official instructions](https://github.com/open-mmlab/mmdetection/)
Going to the specific algorithm's directory to see more details.
## Problem solution and collection
For the problems existing in the process of installation and researching, we will reasonably collect them and provide corresponding solutions. Please refer to [FAQ.md](./docs/FAQ.md) for details.
## Changelog
DavarOCR v0.6.0 was released in 13/07/2022.
Please refer to [Changelog.md](./docs/Changelog.md) for details and release history.
## Citation
If you find this repository is helpful to your research, please feel free to cite us:
``` markdown
@inproceedings{qiao2022davarocr,
title ={{DavarOCR:} {A} Toolbox for OCR and Multi-Modal Document Understanding},
author ={Liang Qiao and
Hui Jiang and
Ying Chen and
Can Li and
Pengfei Li and
Zaisheng Li and
Baorui Zou and
Dashan Guo and
Yingda Xu and
Yunlu Xu and
Zhanzhan Cheng and
Yi Niu}
booktitle = {ACM MM},
pages = {7355--7358},
year = {2022}
}
```
## License
This project is released under the [Apache 2.0 license](./LICENSE)
## Copyright
The copyright of corresponding contributions of our implementations belongs to *Davar-Lab, Hikvision Research Institute, China*, and other codes from open source repository follows the original distributive licenses.
## Welcome to DAVAR-LAB!
See [latest news](https://davar-lab.github.io/) in DAVAR-Lab. If you have any question and suggestion, please feel free to contact us. Contact email: [email protected], [email protected].