@Article{jihpp.2021.017181,
AUTHOR = {Junjie Qu, Jin Liu, Chao Yu},
TITLE = {Adaptive Multi-Scale HyperNet with Bi-Direction Residual Attention Module  for Scene Text Detection},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {83--89},
URL = {http://www.techscience.com/jihpp/v3n2/43993},
ISSN = {2637-4226},
ABSTRACT = {Scene text detection is an important step in the scene text reading 
system. There are still two problems during the existing text detection methods: 
(1) The small receptive of the convolutional layer in text detection is not 
sufficiently sensitive to the target area in the image; (2) The deep receptive of the 
convolutional layer in text detection lose a lot of spatial feature information. 
Therefore, detecting scene text remains a challenging issue. In this work, we 
design an effective text detector named Adaptive Multi-Scale HyperNet
(AMSHN) to improve texts detection performance. Specifically, AMSHN 
enhances the sensitivity of target semantics in shallow features with a new 
attention mechanism to strengthen the region of interest in the image and weaken 
the region of no interest. In addition, it reduces the loss of spatial feature by 
fusing features on multiple paths, which significantly improves the detection 
performance of text. Experimental results on the Robust Reading Challenge on 
Reading Chinese Text on Signboard (ReCTS) dataset show that the proposed 
method has achieved the state-of-the-art results, which proves the ability of our 
detector on both particularity and universality applications.},
DOI = {10.32604/jihpp.2021.017181}
}