  Scene text removal (STR) is a challenging task due to the complex text fonts,
colors, sizes, and background textures in scene images. However, most previous
methods learn both text location and background inpainting implicitly within a
single network, which weakens the text localization mechanism and makes a lossy
background. To tackle these problems, we propose a simple Progressive
Segmentation-guided Scene Text Removal Network(PSSTRNet) to remove the text in
the image iteratively. It contains two decoder branches, a text segmentation
branch, and a text removal branch, with a shared encoder. The text segmentation
branch generates text mask maps as the guidance for the regional removal
branch. In each iteration, the original image, previous text removal result,
and text mask are input to the network to extract the rest part of the text
segments and cleaner text removal result. To get a more accurate text mask map,
an update module is developed to merge the mask map in the current and previous
stages. The final text removal result is obtained by adaptive fusion of results
from all previous stages. A sufficient number of experiments and ablation
studies conducted on the real and synthetic public datasets demonstrate our
proposed method achieves state-of-the-art performance. The source code of our
work is available at:
\href{https://github.com/GuangtaoLyu/PSSTRNet}{https://github.com/GuangtaoLyu/PSSTRNet.}
