  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.
