  Scene text erasing, which replaces text regions with reasonable content in
natural images, has drawn significant attention in the computer vision
community in recent years. There are two potential subtasks in scene text
erasing: text detection and image inpainting. Both subtasks require
considerable data to achieve better performance; however, the lack of a
large-scale real-world scene-text removal dataset does not allow existing
methods to realize their potential. To compensate for the lack of pairwise
real-world data, we made considerable use of synthetic text after additional
enhancement and subsequently trained our model only on the dataset generated by
the improved synthetic text engine. Our proposed network contains a stroke mask
prediction module and background inpainting module that can extract the text
stroke as a relatively small hole from the cropped text image to maintain more
background content for better inpainting results. This model can partially
erase text instances in a scene image with a bounding box or work with an
existing scene-text detector for automatic scene text erasing. The experimental
results from the qualitative and quantitative evaluation on the SCUT-Syn,
ICDAR2013, and SCUT-EnsText datasets demonstrate that our method significantly
outperforms existing state-of-the-art methods even when they are trained on
real-world data.
