Open Access
ARTICLE
CTSF: An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature
Hengyang Wang, Jin Liu*, Haoliang Ren
Shanghai Maritime University, Shanghai, 201306, China
* Corresponding Author: Jin Liu. Email:
Journal on Big Data 2021, 3(3), 119-126. https://doi.org/10.32604/jbd.2021.017184
Received 23 January 2021; Accepted 27 June 2021; Issue published 22 November 2021
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%.
Keywords
Cite This Article
H. Wang, Jin Liu and H. Ren, "Ctsf: an end-to-end efficient neural network for chinese text with skeleton feature,"
Journal on Big Data, vol. 3, no.3, pp. 119–126, 2021.