# Scene Text Eraser

arXiv: https://arxiv.org/abs/1705.02772 · HTML: https://mlsanity.com/paper/1705.02772/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 26 of 26)

- [De-rendering Stylized Texts](https://mlsanity.com/paper/2110.01890/) (2021)
- [Stroke-Based Scene Text Erasing Using Synthetic Data for Training](https://mlsanity.com/paper/2104.11493/) (2021)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context](https://mlsanity.com/paper/2207.10273/) (2022)
- [PERT: A Progressively Region-based Network for Scene Text Removal](https://mlsanity.com/paper/2106.13029/) (2021)
- [The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://mlsanity.com/paper/2210.07489/) (2022)
- [FETNet: Feature Erasing and Transferring Network for Scene Text Removal](https://mlsanity.com/paper/2306.09593/) (2023)
- [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)
- [RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles](https://mlsanity.com/paper/2107.11041/) (2021)
- [Self-Supervised Text Erasing with Controllable Image Synthesis](https://mlsanity.com/paper/2204.12743/) (2022)
- [Progressive Scene Text Erasing with Self-Supervision](https://mlsanity.com/paper/2207.11469/) (2022)
- [PSSTRNet: Progressive Segmentation-guided Scene Text Removal Network](https://mlsanity.com/paper/2306.07842/) (2023)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (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)
- [TPFNet: A Novel Text In-painting Transformer for Text Removal](https://mlsanity.com/paper/2210.14461/) (2022)
- [Selective Scene Text Removal](https://mlsanity.com/paper/2309.00410/) (2023)
- [TextWand: A Unified Framework for Scene Text Editing](https://mlsanity.com/paper/2606.05730/) (2026)
- [MSLKANet: A Multi-Scale Large Kernel Attention Network for Scene Text Removal](https://mlsanity.com/paper/2211.06565/) (2022)
- [Text Image Generation for Low-Resource Languages with Dual Translation Learning](https://mlsanity.com/paper/2409.17747/) (2024)
- [TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images](https://mlsanity.com/paper/2411.00355/) (2024)
- [Inverse Scene Text Removal](https://mlsanity.com/paper/2506.21002/) (2025)
- [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 character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images.