Visual text, a pivotal element in both document and scene images, speaks volumes and attracts significant attention in the computer vision domain. Beyond visual text detection and recognition, the field of visual text processing has experienced a surge in research, driven by the advent of fundamental generative models. However, challenges persist due to the unique properties and features that distinguish text from general objects. Effectively leveraging these unique textual characteristics is crucial in visual text processing, as observed in our study. In this survey, we present a comprehensive, multi-perspective analysis of recent advancements in this field. Initially, we introduce a hierarchical taxonomy encompassing areas ranging from text image enhancement and restoration to text image manipulation, followed by different learning paradigms. Subsequently, we conduct an in-depth discussion of how specific textual features such as structure, stroke, semantics, style, and spatial context are seamlessly integrated into various tasks. Furthermore, we explore available public datasets and benchmark the reviewed methods on several widely-used datasets. Finally, we identify principal challenges and potential avenues for future research. Our aim is to establish this survey as a fundamental resource, fostering continued exploration and innovation in the dynamic area of visual text processing.

翻译：视觉文本作为文档图像与场景图像中的关键元素，承载着丰富的信息并在计算机视觉领域备受关注。在视觉文本检测与识别之外，受基础生成模型发展的驱动，视觉文本处理领域的研究呈现爆发式增长。然而，由于文本区别于通用物体的独特属性与特征，该领域仍面临诸多挑战。本研究发现，有效利用这些独特的文本特征在视觉文本处理中至关重要。本文从多视角对该领域最新进展进行了全面综述。首先，我们构建了从文本图像增强与修复到文本图像操纵的分层分类体系，并系统梳理了不同学习范式。其次，深入探讨了如何将结构、笔画、语义、风格、空间上下文等特定文本特征无缝整合至各类任务中。此外，我们调研了公开数据集，并在多个广泛使用的基准上对所述方法进行了评测。最后，我们指出了当前面临的主要挑战与未来潜在研究方向。本综述旨在为该领域的基础研究提供参考资源，推动视觉文本处理这一动态领域的持续探索与创新。