https://github.com/ayumiymk/aster.pytorch
ASTER in Pytorch
https://github.com/ayumiymk/aster.pytorch
aster computer-vision ocr pytorch scene-text text-recognition text-rectification
Last synced: about 1 month ago
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ASTER in Pytorch
- Host: GitHub
- URL: https://github.com/ayumiymk/aster.pytorch
- Owner: ayumiymk
- License: mit
- Created: 2019-07-13T06:56:34.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-09T11:26:06.000Z (over 3 years ago)
- Last Synced: 2025-03-28T19:10:03.929Z (about 1 month ago)
- Topics: aster, computer-vision, ocr, pytorch, scene-text, text-recognition, text-rectification
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 676
- Watchers: 11
- Forks: 168
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ASTER: Attentional Scene Text Recognizer with Flexible Rectification
This repository implements the ASTER in pytorch. Origin software could be found in [here](https://github.com/bgshih/aster).
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found [here](https://ieeexplore.ieee.org/abstract/document/8395027/).
## Installation
```
conda env create -f environment.yml
```
## Train
[**NOTE**] Some users say that they can't reproduce the reported performance with minor modification, like [1](https://github.com/ayumiymk/aster.pytorch/issues/17#issuecomment-527380815) and [2](https://github.com/ayumiymk/aster.pytorch/issues/17#issuecomment-528718596). I haven't try other settings, so I can't guarantee the same performance with different settings. The users should just run the following script without any modification to reproduce the results.
```
bash scripts/stn_att_rec.sh
```
## Test
You can test with .lmdb files by
```
bash scripts/main_test_all.sh
```
Or test with single image by
```
bash scripts/main_test_image.sh
```
## Pretrained model
The pretrained model is available on our [release page](https://github.com/ayumiymk/aster.pytorch/releases/download/v1.0/demo.pth.tar). Download `demo.pth.tar` and put it to somewhere. Before running, modify the `--resume` to the location of this file.
## Reproduced results
| | IIIT5k | SVT | IC03 | IC13 | IC15 | SVTP | CUTE |
|:-------------:|:------:|:----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| ASTER (L2R) | 92.67 | - | 93.72 | 90.74 | - | 78.76 | 76.39 |
| ASTER.Pytorch | 93.2 | 89.2 | 92.2 | 91 | 78.0 | 81.2 | 81.9 |
At present, the bidirectional attention decoder proposed in ASTER is not included in my implementation.
You can use the codes to bootstrap for your next text recognition research project.
## Data preparation
We give an example to construct your own datasets. Details please refer to `tools/create_svtp_lmdb.py`.
We also provide datasets for [training](https://pan.baidu.com/s/1BMYb93u4gW_3GJdjBWSCSw&shfl=sharepset) (password: wi05) and [testing](https://drive.google.com/open?id=1U4mGLlsm9Ade1-gQOyd6He5R0yiaafYJ).
## Citation
If you find this project helpful for your research, please cite the following papers:
```
@article{bshi2018aster,
author = {Baoguang Shi and
Mingkun Yang and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {ASTER: An Attentional Scene Text Recognizer with Flexible Rectification},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {41},
number = {9},
pages = {2035--2048},
year = {2019},
}
@inproceedings{ShiWLYB16,
author = {Baoguang Shi and
Xinggang Wang and
Pengyuan Lyu and
Cong Yao and
Xiang Bai},
title = {Robust Scene Text Recognition with Automatic Rectification},
booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
pages = {4168--4176},
year = {2016}
}
```
IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you [contact us]([email protected]) regarding possible patent issues.