# Exploring Stroke-Level Modifications for Scene Text Editing

arXiv: https://arxiv.org/abs/2212.01982 · HTML: https://mlsanity.com/paper/2212.01982/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [SVTR: Scene Text Recognition with a Single Visual Model](https://mlsanity.com/paper/2205.00159/) (2022)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://mlsanity.com/paper/1806.00926/) (2018)
- [SwapText: Image Based Texts Transfer in Scenes](https://mlsanity.com/paper/2003.08152/) (2020)
- [STEFANN: Scene Text Editor using Font Adaptive Neural Network](https://mlsanity.com/paper/1903.01192/) (2019)
- [Controllable Artistic Text Style Transfer via Shape-Matching GAN](https://mlsanity.com/paper/1905.01354/) (2019)
- [Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes](https://mlsanity.com/paper/1807.03021/) (2018)
- [Learning to Write Stylized Chinese Characters by Reading a Handful of Examples](https://mlsanity.com/paper/1712.06424/) (2017)
- [Awesome Typography: Statistics-Based Text Effects Transfer](https://mlsanity.com/paper/1611.09026/) (2016)
- [Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection](https://mlsanity.com/paper/2109.03426/) (2021)
- [TET-GAN: Text Effects Transfer via Stylization and Destylization](https://mlsanity.com/paper/1812.06384/) (2018)
- [Deep Learning-based Forgery Attack on Document Images](https://mlsanity.com/paper/2102.00653/) (2021)
- [PERT: A Progressively Region-based Network for Scene Text Removal](https://mlsanity.com/paper/2106.13029/) (2021)
- [STRIVE: Scene Text Replacement In Videos](https://mlsanity.com/paper/2109.02762/) (2021)

## Built on by (most cited first, 30 of 37)

- [TextDiffuser: Diffusion Models as Text Painters](https://mlsanity.com/paper/2305.10855/) (2023)
- [UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models](https://mlsanity.com/paper/2312.04884/) (2023)
- [Improving Diffusion Models for Scene Text Editing with Dual Encoders](https://mlsanity.com/paper/2304.05568/) (2023)
- [DiffUTE: Universal Text Editing Diffusion Model](https://mlsanity.com/paper/2305.10825/) (2023)
- [TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control](https://mlsanity.com/paper/2410.10133/) (2024)
- [Revisiting Tampered Scene Text Detection in the Era of Generative AI](https://mlsanity.com/paper/2407.21422/) (2024)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [DreamText: High Fidelity Scene Text Synthesis](https://mlsanity.com/paper/2405.14701/) (2024)
- [Recognition-Synergistic Scene Text Editing](https://mlsanity.com/paper/2503.08387/) (2025)
- [GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing](https://mlsanity.com/paper/2505.04915/) (2025)
- [Kinetic Typography Diffusion Model](https://mlsanity.com/paper/2407.10476/) (2024)
- [How Control Information Influences Multilingual Text Image Generation and Editing?](https://mlsanity.com/paper/2407.11502/) (2024)
- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
- [Text Image Inpainting via Global Structure-Guided Diffusion Models](https://mlsanity.com/paper/2401.14832/) (2024)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition](https://mlsanity.com/paper/2412.01137/) (2024)
- [Type-R: Automatically Retouching Typos for Text-to-Image Generation](https://mlsanity.com/paper/2411.18159/) (2024)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Show Me the World in My Language: Establishing the First Baseline for Scene-Text to Scene-Text Translation](https://mlsanity.com/paper/2308.03024/) (2023)
- [StyleTextGen: Style-Conditioned Multilingual Scene Text Generation](https://mlsanity.com/paper/2605.14708/) (2026)
- [OmniText: A Training-Free Generalist for Controllable Text-Image Manipulation](https://mlsanity.com/paper/2510.24093/) (2025)
- [PSGText: Stroke-Guided Scene Text Editing with PSP Module](https://mlsanity.com/paper/2310.13366/) (2023)
- [Beyond Flat Text: Dual Self-inherited Guidance for Visual Text Generation](https://mlsanity.com/paper/2501.05892/) (2025)
- [What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution](https://mlsanity.com/paper/2602.06450/) (2026)
- [Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion Model](https://mlsanity.com/paper/2311.00496/) (2023)
- [Image Generation and Learning Strategy for Deep Document Forgery Detection](https://mlsanity.com/paper/2311.03650/) (2023)
- [Progressive Evolution from Single-Point to Polygon for Scene Text](https://mlsanity.com/paper/2312.13778/) (2023)
- [Towards Training-Free Scene Text Editing](https://mlsanity.com/paper/2603.24571/) (2026)
- [Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning](https://mlsanity.com/paper/2605.15523/) (2026)
- [Syn3DTxt: Embedding 3D Cues for Scene Text Generation](https://mlsanity.com/paper/2505.18479/) (2025)

## Abstract (arXiv)

Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.