Abstract
Scene Text Editing (STE) involves replacing text in a scene image with new target text while preserving both the original text style and background texture. Existing methods suffer from two major challenges: inconsistency and length-insensitivity. They often fail to maintain coherence between the edited local patch and the surrounding area, and they struggle to handle significant differences in text length before and after editing. To tackle these challenges, we propose an end-to-end framework called Global-Local Aware Scene Text Editing (GLASTE), which simultaneously incorporates high-level global contextual information along with delicate local features. Specifically, we design a global-local combination structure, joint global and local losses, and enhance text image features to ensure consistency in text style within local patches while maintaining harmony between local and global areas. Additionally, we express the text style as a vector independent of the image size, which can be transferred to target text images of various sizes. We use an affine fusion to fill target text images into the editing patch while maintaining their aspect ratio unchanged. Extensive experiments on real-world datasets validate that our GLASTE model outperforms previous methods in both quantitative metrics and qualitative results and effectively mitigates the two challenges.
Abstract (translated)
场景文本编辑（STE）涉及在保留原始文本样式和背景纹理的同时，将场景图像中的文字替换为新的目标文字。现有方法面临两大挑战：不一致性及长度敏感性问题。它们通常无法维持编辑后的局部区域与其周围环境之间的连贯性，并且难以处理编辑前后的文本长度显著变化的问题。 为了克服这些挑战，我们提出了一种名为全局-局部感知场景文本编辑（GLASTE）的端到端框架，该框架同时整合了高层次的全局上下文信息和精细的局部特征。具体而言，我们设计了一个全局-局部组合结构，并引入联合全局与局部损失函数以确保在保持局部区域之间和谐的同时，在局部区域内维持一致的文字风格。 此外，我们将文字样式表示为独立于图像大小的向量形式，这使得它可以转移到各种尺寸的目标文本图像中。我们在填充目标文本到编辑区域时采用仿射融合技术来保留其纵横比不变。 通过真实世界数据集上的广泛实验验证了GLASTE模型在量化指标和定性结果上均优于先前的方法，并且有效缓解了上述两大挑战。
URL
https://arxiv.org/abs/2512.03574
