Shortcuts

Text Detection Models

Real-time Scene Text Detection with Differentiable Binarization

Introduction

[ALGORITHM]

@article{Liao_Wan_Yao_Chen_Bai_2020,
    title={Real-Time Scene Text Detection with Differentiable Binarization},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
    year={2020},
    pages={11474-11481}}

Results and models

ICDAR2015

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
DBNet_r18 ImageNet ICDAR2015 Train ICDAR2015 Test 1200 736 0.731 0.871 0.795 model | log
DBNet_r50dcn Synthtext ICDAR2015 Train ICDAR2015 Test 1200 1024 0.796 0.866 0.830 model | log

DRRG

Introduction

[ALGORITHM]

@article{zhang2020drrg,
  title={Deep relational reasoning graph network for arbitrary shape text detection},
  author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
  booktitle={CVPR},
  pages={9699-9708},
  year={2020}
}

Results and models

CTW1500

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
DRRG ImageNet CTW1500 Train CTW1500 Test 1200 640 0.822 (0.791) 0.858 (0.862) 0.840 (0.825) model \ log

Note: We’ve upgraded our IoU backend from Polygon3 to shapely. There are some performance differences for some models due to the backends’ different logics to handle invalid polygons (more info here). New evaluation result is presented in brackets and new logs will be uploaded soon.

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

Introduction

[ALGORITHM]

@InProceedings{zhu2021fourier,
      title={Fourier Contour Embedding for Arbitrary-Shaped Text Detection},
      author={Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang},
      year={2021},
      booktitle = {CVPR}
      }

Results and models

CTW1500

Method Backbone Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
FCENet ResNet50 + DCNv2 ImageNet CTW1500 Train CTW1500 Test 1500 (736, 1080) 0.828 0.875 0.851 model | log

ICDAR2015

Method Backbone Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
FCENet ResNet50 ImageNet IC15 Train IC15 Test 1500 (2260, 2260) 0.819 0.880 0.849 model | log

Mask R-CNN

Introduction

[ALGORITHM]

@INPROCEEDINGS{8237584,
  author={K. {He} and G. {Gkioxari} and P. {Dollár} and R. {Girshick}},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)},
  title={Mask R-CNN},
  year={2017},
  pages={2980-2988},
  doi={10.1109/ICCV.2017.322}}

In tuning parameters, we refer to the baseline method in the following article:

@article{pmtd,
  author={Jingchao Liu and Xuebo Liu and Jie Sheng and Ding Liang and Xin Li and Qingjie Liu},
  title={Pyramid Mask Text Detector},
  journal={CoRR},
  volume={abs/1903.11800},
  year={2019}
}

Results and models

CTW1500

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
MaskRCNN ImageNet CTW1500 Train CTW1500 Test 160 1600 0.753 0.712 0.732 model | log

ICDAR2015

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
MaskRCNN ImageNet ICDAR2015 Train ICDAR2015 Test 160 1920 0.783 0.872 0.825 model | log

ICDAR2017

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
MaskRCNN ImageNet ICDAR2017 Train ICDAR2017 Val 160 1600 0.754 0.827 0.789 model | log

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Introduction

[ALGORITHM]

@inproceedings{WangXSZWLYS19,
  author={Wenhai Wang and Enze Xie and Xiaoge Song and Yuhang Zang and Wenjia Wang and Tong Lu and Gang Yu and Chunhua Shen},
  title={Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network},
  booktitle={ICCV},
  pages={8439--8448},
  year={2019}
  }

Results and models

CTW1500

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
PANet ImageNet CTW1500 Train CTW1500 Test 600 640 0.776 (0.717) 0.838 (0.835) 0.806 (0.801) model | log

ICDAR2015

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
PANet ImageNet ICDAR2015 Train ICDAR2015 Test 600 736 0.734 (0.74) 0.856 (0.86) 0.791 (0.795) model | log

Note: We’ve upgraded our IoU backend from Polygon3 to shapely. There are some performance differences for some models due to the backends’ different logics to handle invalid polygons (more info here). New evaluation result is presented in brackets and new logs will be uploaded soon.

PSENet

Introduction

[ALGORITHM]

@inproceedings{wang2019shape,
  title={Shape robust text detection with progressive scale expansion network},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9336--9345},
  year={2019}
}

Results and models

CTW1500

Method Backbone Extra Data Training set Test set ##epochs Test size Recall Precision Hmean Download
PSENet-4s ResNet50 - CTW1500 Train CTW1500 Test 600 1280 0.728 (0.717) 0.849 (0.852) 0.784 (0.779) model | log

ICDAR2015

Method Backbone Extra Data Training set Test set ##epochs Test size Recall Precision Hmean Download
PSENet-4s ResNet50 - IC15 Train IC15 Test 600 2240 0.784 (0.753) 0.831 (0.867) 0.807 (0.806) model | log
PSENet-4s ResNet50 pretrain on IC17 MLT model IC15 Train IC15 Test 600 2240 0.834 0.861 0.847 model | log

Note: We’ve upgraded our IoU backend from Polygon3 to shapely. There are some performance differences for some models due to the backends’ different logics to handle invalid polygons (more info here). New evaluation result is presented in brackets and new logs will be uploaded soon.

Textsnake

Introduction

[ALGORITHM]

@article{long2018textsnake,
  title={TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes},
  author={Long, Shangbang and Ruan, Jiaqiang and Zhang, Wenjie and He, Xin and Wu, Wenhao and Yao, Cong},
  booktitle={ECCV},
  pages={20-36},
  year={2018}
}

Results and models

CTW1500

Method Pretrained Model Training set Test set ##epochs Test size Recall Precision Hmean Download
TextSnake ImageNet CTW1500 Train CTW1500 Test 1200 736 0.795 0.840 0.817 model | log
Read the Docs v: latest
Versions
latest
stable
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.