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- W4367315935 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." @default.
- W4367315935 created "2023-04-29" @default.
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- W4367315935 date "2023-08-01" @default.
- W4367315935 modified "2023-09-23" @default.
- W4367315935 title "Progressive scene text erasing with self-supervision" @default.
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- W4367315935 doi "https://doi.org/10.1016/j.cviu.2023.103712" @default.
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