# MTRNet++: One-stage Mask-based Scene Text Eraser

arXiv: https://arxiv.org/abs/1912.07183 · HTML: https://mlsanity.com/paper/1912.07183/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 24 of 24)

- [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)
- [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)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [Scene Style Text Editing](https://mlsanity.com/paper/2304.10097/) (2023)
- [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)
- [CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal](https://mlsanity.com/paper/2603.21901/) (2026)
- [TextWand: A Unified Framework for Scene Text Editing](https://mlsanity.com/paper/2606.05730/) (2026)
- [Artistic-style text detector and a new Movie-Poster dataset](https://mlsanity.com/paper/2406.16307/) (2024)
- [Leveraging Text Localization for Scene Text Removal via Text-aware Masked Image Modeling](https://mlsanity.com/paper/2409.13431/) (2024)
- [MSLKANet: A Multi-Scale Large Kernel Attention Network for Scene Text Removal](https://mlsanity.com/paper/2211.06565/) (2022)
- [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)

A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++. It has a novel architecture that includes mask-refine, coarse-inpainting and fine-inpainting branches, and attention blocks. With this architecture, MTRNet++ can remove text either with or without an external mask. It achieves state-of-the-art results on both the Oxford and SCUT datasets without using external ground-truth masks. The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential. It also demonstrates controllability and interpretability.