FCENet

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

Abstract

One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. To tackle these problems, we model text instances in the Fourier domain and propose one novel Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours as compact signatures. We further construct FCENet with a backbone, feature pyramid networks (FPN) and a simple post-processing with the Inverse Fourier Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous methods, FCENet first predicts compact Fourier signatures of text instances, and then reconstructs text contours via IFT and NMS during test. Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection. Furthermore, experimental results show that our FCENet is superior to the state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on challenging highly-curved text subset.

Results and models

CTW1500

Method	Backbone	Pretrained Model	Training set	Test set	#epochs	Test size	Precision	Recall	Hmean	Download
FCENet_r50dcn	ResNet50 + DCNv2	-	CTW1500 Train	CTW1500 Test	1500	(736, 1080)	0.8689	0.8296	0.8488	model \| log
FCENet_r50-oclip	ResNet50-oCLIP	-	CTW1500 Train	CTW1500 Test	1500	(736, 1080)	0.8383	0.801	0.8192	model \| log

ICDAR2015

Method	Backbone	Pretrained Model	Training set	Test set	#epochs	Test size	Precision	Recall	Hmean	Download
FCENet_r50	ResNet50	-	IC15 Train	IC15 Test	1500	(2260, 2260)	0.8243	0.8834	0.8528	model \| log
FCENet_r50-oclip	ResNet50-oCLIP	-	IC15 Train	IC15 Test	1500	(2260, 2260)	0.9176	0.8098	0.8604	model \| log

Total Text

Method	Backbone	Pretrained Model	Training set	Test set	#epochs	Test size	Precision	Recall	Hmean	Download
FCENet_r50	ResNet50	-	Totaltext Train	Totaltext Test	1500	(1280, 960)	0.8485	0.7810	0.8134	model \| log

Citation

@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}
      }