  The scene text removal (STR) task aims to remove text regions and recover the
background smoothly in images for private information protection. Most existing
STR methods adopt encoder-decoder-based CNNs, with direct copies of the
features in the skip connections. However, the encoded features contain both
text texture and structure information. The insufficient utilization of text
features hampers the performance of background reconstruction in text removal
regions. To tackle these problems, we propose a novel Feature Erasing and
Transferring (FET) mechanism to reconfigure the encoded features for STR in
this paper. In FET, a Feature Erasing Module (FEM) is designed to erase text
features. An attention module is responsible for generating the feature
similarity guidance. The Feature Transferring Module (FTM) is introduced to
transfer the corresponding features in different layers based on the attention
guidance. With this mechanism, a one-stage, end-to-end trainable network called
FETNet is constructed for scene text removal. In addition, to facilitate
research on both scene text removal and segmentation tasks, we introduce a
novel dataset, Flickr-ST, with multi-category annotations. A sufficient number
of experiments and ablation studies are conducted on the public datasets and
Flickr-ST. Our proposed method achieves state-of-the-art performance using most
metrics, with remarkably higher quality scene text removal results. The source
code of our work is available at:
\href{https://github.com/GuangtaoLyu/FETNet}{https://github.com/GuangtaoLyu/FETNet.
