  Scene text erasing, which replaces text regions with reasonable content in
natural images, has drawn attention in the computer vision community in recent
years. There are two potential subtasks in scene text erasing: text detection
and image inpainting. Either sub-task requires considerable data to achieve
better performance; however, the lack of a large-scale real-world scene-text
removal dataset allows the existing methods to not work in full strength. To
avoid the limitation of the lack of pairwise real-world data, we enhance and
make full use of the synthetic text and consequently train 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
text image patch to maintain more background content for better inpainting
results. This model can partially erase text instances in a scene image with a
bounding box provided or work with an existing scene text detector for
automatic scene text erasing. The experimental results of qualitative
evaluation 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 trained on real-world data.
