Text Region Multiple Information Perception Network for Scene Text
  Detection
Jinzhi Zheng Libo Zhang Yanjun Wu Chen Zhao
Segmentation-based scene text detection algorithms can handle arbitrary shape
scene texts and have strong robustness and adaptability, so it has attracted
wide attention. Existing segmentation-based scene text detection algorithms
usually only segment the pixels in the center region of the text, while
ignoring other information of the text region, such as edge information,
distance information, etc., thus limiting the detection accuracy of the
algorithm for scene text. This paper proposes a plug-and-play module called the
Region Multiple Information Perception Module (RMIPM) to enhance the detection
performance of segmentation-based algorithms. Specifically, we design an
improved module that can perceive various types of information about scene text
regions, such as text foreground classification maps, distance maps, direction
maps, etc. Experiments on MSRA-TD500 and TotalText datasets show that our
method achieves comparable performance with current state-of-the-art
algorithms.