@Article{jbd.2021.017184,
AUTHOR = {Hengyang Wang, Jin Liu, Haoliang Ren},
TITLE = {CTSF: An End-to-End Efficient Neural Network for Chinese Text with  Skeleton Feature},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {119--126},
URL = {http://www.techscience.com/jbd/v3n3/45673},
ISSN = {2579-0056},
ABSTRACT = {The past decade has seen the rapid development of text detection based on 
deep learning. However, current methods of Chinese character detection and 
recognition have proven to be poor. The accuracy of segmenting text boxes in natural 
scenes is not impressive. The reasons for this strait can be summarized into two points: 
the complexity of natural scenes and numerous types of Chinese characters. In 
response to these problems, we proposed a lightweight neural network architecture 
named CTSF. It consists of two modules, one is a text detection network that 
combines CTPN and the image feature extraction modules of PVANet, named CDSE. 
The other is a literacy network based on spatial pyramid pool and fusion of Chinese 
character skeleton features named SPPCNN-SF, so as to realize the text detection and 
recognition, respectively. Our model performs much better than the original model 
on ICDAR2011 and ICDAR2013 (achieved 85% and 88% F-measures) and 
enhanced the processing speed in training phase. In addition, our method achieves 
extremely performance on three Chinese datasets, with accuracy of 95.12%, 95.56% 
and 96.01%.},
DOI = {10.32604/jbd.2021.017184}
}