@Article{cmc.2020.09471,
AUTHOR = {Shaozhang Niu, Xiangxiang Li, Maosen Wang, Yueying Li},
TITLE = {A Modified Method for Scene Text Detection by ResNet},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2233--2245},
URL = {http://www.techscience.com/cmc/v65n3/40166},
ISSN = {1546-2226},
ABSTRACT = {In recent years, images have played a more and more important role in our 
daily life and social communication. To some extent, the textual information contained in 
the pictures is an important factor in understanding the content of the scenes themselves. The more accurate the text detection of the natural scenes is, the more accurate our 
semantic understanding of the images will be. Thus, scene text detection has also become
the hot spot in the domain of computer vision. In this paper, we have presented a 
modified text detection network which is based on further research and improvement of 
Connectionist Text Proposal Network (CTPN) proposed by previous researchers. To 
extract deeper features that are less affected by different images, we use Residual 
Network (ResNet) to replace Visual Geometry Group Network (VGGNet) which is used
in the original network. Meanwhile, to enhance the robustness of the models to multiple 
languages, we use the datasets for training from multi-lingual scene text detection and 
script identification datasets (MLT) of 2017 International Conference on Document 
Analysis and Recognition (ICDAR2017). And apart from that, the attention mechanism is 
used to get more reasonable weight distribution. We found the proposed models achieve
0.91 F1-score on ICDAR2011 test, better than CTPN trained on the same datasets by
about 5%.},
DOI = {10.32604/cmc.2020.09471}
}