PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
PlugNet combines the pluggable super-resolution unit (PSU) to solve the low-quality text recognition from the feature-leve. The research paper can be found here. The presentation of this paper refer to TechBeat.
Note: Due to business competition, we only open some relevant core code for reference and communication of relevant researchers. Pretraining models and specific training methods cannot be provided in open source at present.
Reproduced results
| IIIT5k | SVT | IC03 | IC13 | SVTP | CUTE | |
|---|---|---|---|---|---|---|
| ASTER.Pytorch | 93.2 | 89.2 | 92.2 | 91 | 81.2 | 81.9 |
| Aster(our training) | 93.4 | 89.5 | 94.5 | 91.8 | 78.5 | 79.5 |
| PlugNet | 94.4 | 92.3 | 95.7 | 95.0 | 84.3 | 85.0 |
You can use the codes to bootstrap for your next text recognition research project.
Citation
If you find this project helpful for your research, please cite the following papers:
@article{eccv2020plugnet,
author = {Yongqiang Mou and
Lei Tan and
Hui Yang and
Jingying Chen and
Leyuan Liu and
Rui Yan and
Yaohong Huang},
title = {PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit},
journal = {The 16th European Conference on Computer Vision (ECCV 2020), 2020.},
volume = {},
number = {},
pages = {1-17},
year = {2020},
}
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.