# PSGText: Stroke-Guided Scene Text Editing with PSP Module

arXiv: https://arxiv.org/abs/2310.13366 · HTML: https://mlsanity.com/paper/2310.13366/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [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)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [Diversified Texture Synthesis with Feed-forward Networks](https://mlsanity.com/paper/1703.01664/) (2017)
- [SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models](https://mlsanity.com/paper/2107.09313/) (2021)
- [Exploring Stroke-Level Modifications for Scene Text Editing](https://mlsanity.com/paper/2212.01982/) (2022)
- [Stroke-Based Scene Text Erasing Using Synthetic Data for Training](https://mlsanity.com/paper/2104.11493/) (2021)
- [Generating Synthetic Data for Text Recognition](https://mlsanity.com/paper/1608.04224/) (2016)

## Built on by (most cited first, 2 of 2)

- [Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection](https://mlsanity.com/paper/2505.07309/) (2025)
- [Multimodal Chip Physical Design Engineer Assistant](https://mlsanity.com/paper/2510.15872/) (2025)

## Abstract (arXiv)

Scene Text Editing (STE) aims to substitute text in an image with new desired text while preserving the background and styles of the original text. However, present techniques present a notable challenge in the generation of edited text images that exhibit a high degree of clarity and legibility. This challenge primarily stems from the inherent diversity found within various text types and the intricate textures of complex backgrounds. To address this challenge, this paper introduces a three-stage framework for transferring texts across text images. Initially, we introduce a text-swapping network that seamlessly substitutes the original text with the desired replacement. Subsequently, we incorporate a background inpainting network into our framework. This specialized network is designed to skillfully reconstruct background images, effectively addressing the voids left after the removal of the original text. This process meticulously preserves visual harmony and coherence in the background. Ultimately, the synthesis of outcomes from the text-swapping network and the background inpainting network is achieved through a fusion network, culminating in the creation of the meticulously edited final image. A demo video is included in the supplementary material.