# FETNet: Feature Erasing and Transferring Network for Scene Text Removal

arXiv: https://arxiv.org/abs/2306.09593 · HTML: https://mlsanity.com/paper/2306.09593/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 27 of 27)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text](https://mlsanity.com/paper/2105.05486/) (2021)
- [Recurrent Feature Reasoning for Image Inpainting](https://mlsanity.com/paper/2008.03737/) (2020)
- [Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations](https://mlsanity.com/paper/2007.06929/) (2020)
- [SwapText: Image Based Texts Transfer in Scenes](https://mlsanity.com/paper/2003.08152/) (2020)
- [Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach](https://mlsanity.com/paper/2011.14021/) (2020)
- [MTRNet: A Generic Scene Text Eraser](https://mlsanity.com/paper/1903.04092/) (2019)
- [EnsNet: Ensconce Text in the Wild](https://mlsanity.com/paper/1812.00723/) (2018)
- [Scene Text Eraser](https://mlsanity.com/paper/1705.02772/) (2017)
- [MTRNet++: One-stage Mask-based Scene Text Eraser](https://mlsanity.com/paper/1912.07183/) (2019)
- [Stroke-Based Scene Text Erasing Using Synthetic Data for Training](https://mlsanity.com/paper/2104.11493/) (2021)
- [Scene text removal via cascaded text stroke detection and erasing](https://mlsanity.com/paper/2011.09768/) (2020)
- [Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context](https://mlsanity.com/paper/2207.10273/) (2022)
- [Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation](https://mlsanity.com/paper/1911.09026/) (2019)
- [PSSTRNet: Progressive Segmentation-guided Scene Text Removal Network](https://mlsanity.com/paper/2306.07842/) (2023)

## Built on by (most cited first, 12 of 12)

- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining](https://mlsanity.com/paper/2306.12106/) (2023)
- [UPOCR: Towards Unified Pixel-Level OCR Interface](https://mlsanity.com/paper/2312.02694/) (2023)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [EAFormer: Scene Text Segmentation with Edge-Aware Transformers](https://mlsanity.com/paper/2407.17020/) (2024)
- [DeepEraser: Deep Iterative Context Mining for Generic Text Eraser](https://mlsanity.com/paper/2402.19108/) (2024)
- [DiffSTR: Controlled Diffusion Models for Scene Text Removal](https://mlsanity.com/paper/2410.21721/) (2024)
- [ConText: Driving In-context Learning for Text Removal and Segmentation](https://mlsanity.com/paper/2506.03799/) (2025)
- [SFA: Scan, Focus, and Amplify toward Guidance-aware Answering for Video TextVQA](https://mlsanity.com/paper/2511.20190/) (2025)
- [Leveraging Text Localization for Scene Text Removal via Text-aware Masked Image Modeling](https://mlsanity.com/paper/2409.13431/) (2024)
- [OTR: Synthesizing Overlay Text Dataset for Text Removal](https://mlsanity.com/paper/2510.02787/) (2025)
- [What Shape Is Optimal for Masks in Text Removal?](https://mlsanity.com/paper/2511.22499/) (2025)

## Abstract (arXiv)

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: hrefhttps://github.com/GuangtaoLyu/FETNethttps://github.com/GuangtaoLyu/FETNet.