Progressive Scene Text Erasing with Self-Supervision
- URL: http://arxiv.org/abs/2207.11469v2
- Date: Fri, 28 Apr 2023 09:36:53 GMT
- Title: Progressive Scene Text Erasing with Self-Supervision
- Authors: Xiangcheng Du and Zhao Zhou and Yingbin Zheng and Xingjiao Wu and
Tianlong Ma and Cheng Jin
- Abstract summary: Scene text erasing seeks to erase text contents from scene images.
Current state-of-the-art text erasing models are trained on large-scale synthetic data.
We employ self-supervision for feature representation on unlabeled real-world scene text images.
- Score: 7.118419154170154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scene text erasing seeks to erase text contents from scene images and current
state-of-the-art text erasing models are trained on large-scale synthetic data.
Although data synthetic engines can provide vast amounts of annotated training
samples, there are differences between synthetic and real-world data. In this
paper, we employ self-supervision for feature representation on unlabeled
real-world scene text images. A novel pretext task is designed to keep
consistent among text stroke masks of image variants. We design the Progressive
Erasing Network in order to remove residual texts. The scene text is erased
progressively by leveraging the intermediate generated results which provide
the foundation for subsequent higher quality results. Experiments show that our
method significantly improves the generalization of the text erasing task and
achieves state-of-the-art performance on public benchmarks.
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