ASTER: Attentional Scene Text Recognizer with Flexible Rectification
This repository implements the ASTER in pytorch. Origin software could be found in here.
ASTER is an accurate scene text recognizer with flexible rectification mechanism. The research paper can be found here.
Train
bash scripts/stn_att_rec.sh
Test
bash scripts/main_test_all.sh
Reproduced results
IIIT5k | SVT | IC03 | IC13 | SVTP | CUTE | |
---|---|---|---|---|---|---|
ASTER (L2R) | 92.67 | - | 93.72 | 90.74 | 78.76 | 76.39 |
ASTER.Pytorch | 93.2 | 89.2 | 92.2 | 91 | 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
.
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 = {},
number = {},
pages = {1-1},
year = {2018},
}
@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 regarding possible patent issues.