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

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	0.838	0.806	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	0.856	0.791	model \| log

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	0.849	0.784	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	0.831	0.807	model \| log
PSENet-4s	ResNet50	pretrain on IC17 MLT model	IC15 Train	IC15 Test	600	2240	0.834	0.861	0.847	model \| log

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