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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3018859
|2 doi
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|a pubmed25n1047.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Liu, Chongyu
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a EraseNet
|b End-to-End Text Removal in the Wild
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| 264 |
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|c 2020
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 22.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a Scene text removal has attracted increasing research interests owing to its valuable applications in privacy protection, camera-based virtual reality translation, and image editing. However, existing approaches, which fall short on real applications, are mainly because they were evaluated on synthetic or unrepresentative datasets. To fill this gap and facilitate this research direction, this paper proposes a real-world dataset called SCUT-EnsText that consists of 3,562 diverse images selected from public scene text reading benchmarks, and each image is scrupulously annotated to provide visually plausible erasure targets. With SCUT-EnsText, we design a novel GANbased model termed EraseNet that can automatically remove text located on the natural images. The model is a two-stage network that consists of a coarse-erasure sub-network and a refinement sub-network. The refinement sub-network targets improvement in the feature representation and refinement of the coarse outputs to enhance the removal performance. Additionally, EraseNet contains a segmentation head for text perception and a local-global SN-Patch-GAN with spectral normalization (SN) on both the generator and discriminator for maintaining the training stability and the congruity of the erased regions. A sufficient number of experiments are conducted on both the previous public dataset and the brand-new SCUT-EnsText. Our EraseNet significantly outperforms the existing state-of-the-art methods in terms of all metrics, with remarkably superior higherquality results. The dataset and code will be made available at https://github.com/HCIILAB/SCUT-EnsText
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Liu, Yuliang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jin, Lianwen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Shuaitao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Canjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yongpan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2020) vom: 28. Aug.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
1 |
8 |
|g volume:PP
|g year:2020
|g day:28
|g month:08
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TIP.2020.3018859
|3 Volltext
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