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.

本文提出了一种名为Feature Erasing and Transferring（FET）的机制，用于重新配置编码特征以进行场景文本去除，并构建了一个名为FETNet的一阶段端到端可训练网络；此外，作者还介绍了一个名为Flickr-ST的新数据集，其中包含多类别注释。在公共数据集和Flickr-ST上进行了充分数量的实验和消融研究，证明了该方法在大多数度量标准下均实现了最先进的性能，且场景文本去除结果质量显著提高。

FETNet: 场景文本去除的特征擦除和转移网络