  Recent learning-based approaches show promising performance improvement for
scene text removal task. However, these methods usually leave some remnants of
text and obtain visually unpleasant results. In this work, we propose a novel
"end-to-end" framework based on accurate text stroke detection. Specifically,
we decouple the text removal problem into text stroke detection and stroke
removal. We design a text stroke detection network and a text removal
generation network to solve these two sub-problems separately. Then, we combine
these two networks as a processing unit, and cascade this unit to obtain the
final model for text removal. Experimental results demonstrate that the
proposed method significantly outperforms the state-of-the-art approaches for
locating and erasing scene text. Since current publicly available datasets are
all synthetic and cannot properly measure the performance of different methods,
we therefore construct a new real-world dataset, which will be released to
facilitate the relevant research.
