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}{this https URL}

我们提出了一种简单的 PSSTRNet，用于通过场景文本分割逐步删除图像中的文本，实现了最先进的性能表现。

PSSTRNet：渐进式分割引导的场景文本去除网络

PSSTRNet: Progressive Segmentation-guided Scene Text Removal Network

The assumption of scene rigidity is typical in SLAM algorithms. Such a strong
assumption limits the use of most visual SLAM systems in populated real-world
environments, which are the target of several relevant applications like
service robotics or autonomous vehicles. In this paper we present DynaSLAM, a
visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of
dynamic object detection and background inpainting. DynaSLAM is robust in
dynamic scenarios for monocular, stereo and RGB-D configurations. We are
capable of detecting the moving objects either by multi-view geometry, deep
learning or both. Having a static map of the scene allows inpainting the frame
background that has been occluded by such dynamic objects. We evaluate our
system in public monocular, stereo and RGB-D datasets. We study the impact of
several accuracy/speed trade-offs to assess the limits of the proposed
methodology. DynaSLAM outperforms the accuracy of standard visual SLAM
baselines in highly dynamic scenarios. And it also estimates a map of the
static parts of the scene, which is a must for long-term applications in
real-world environments.

本文提出了 DynaSLAM，这是一种基于 ORB-SLAM2 的视觉 SLAM 系统，可以检测动态对象并背景填充，适用于单目，立体和 RGB-D 配置，并在公共数据集中证明了其在高度动态场景中优于标准视觉 SLAM 基线的准确性。