  Real-world text can be damaged by corrosion issues caused by environmental or
human factors, which hinder the preservation of the complete styles of texts,
e.g., texture and structure. These corrosion issues, such as graffiti signs and
incomplete signatures, bring difficulties in understanding the texts, thereby
posing significant challenges to downstream applications, e.g., scene text
recognition and signature identification. Notably, current inpainting
techniques often fail to adequately address this problem and have difficulties
restoring accurate text images along with reasonable and consistent styles.
Formulating this as an open problem of text image inpainting, this paper aims
to build a benchmark to facilitate its study. In doing so, we establish two
specific text inpainting datasets which contain scene text images and
handwritten text images, respectively. Each of them includes images revamped by
real-life and synthetic datasets, featuring pairs of original images, corrupted
images, and other assistant information. On top of the datasets, we further
develop a novel neural framework, Global Structure-guided Diffusion Model
(GSDM), as a potential solution. Leveraging the global structure of the text as
a prior, the proposed GSDM develops an efficient diffusion model to recover
clean texts. The efficacy of our approach is demonstrated by thorough empirical
study, including a substantial boost in both recognition accuracy and image
quality. These findings not only highlight the effectiveness of our method but
also underscore its potential to enhance the broader field of text image
understanding and processing. Code and datasets are available at:
https://github.com/blackprotoss/GSDM.
