University of Oulu

W. Zhang, L. Zhu, L. Xu, J. Zhou, H. Sun and X. Liu, "Deep Learning Based Container Text Recognition," 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal, 2019, pp. 69-74,

Deep learning based container text recognition

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Author: Zhang, Weishan1; Zhu, Liqian1; Xu, Liang2;
Organizations: 1College of computer and Communication Engineering China University of Petroleum Qingdao, China
2College of computer and Communication Engineering Beijing University of Science and Technology Beijing, China
3University of Oulu Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-04-29


Traditional character segmentation has low accuracy for container scene text recognition. Convolutional recurrent neural network (CRNN) and connectionist text proposal network (CTPN) methods cannot extract container text features effectively. This paper proposes a novel Container Text Detection and Recognition Network (CTDRNet) for accurately detecting and recognizing container scene text. The CTDRNet consists of three components: (1) CTDRNet text detection enables to improve detection accuracy for single words; (2) CTDRNet text recognition has faster convergence speed and detection accuracy; (3) CTDRNet post-processing improves detection and recognition accuracy. In the end, the CTDRNet is implemented and evaluated with an accuracy of 96% and processing rate of 2.5 fps.

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ISBN: 978-1-7281-0350-1
ISBN Print: 978-1-7281-0351-8
Pages: 69 - 74
DOI: 10.1109/CSCWD.2019.8791876
Host publication: 23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
Host publication editor: Luo, Junzhou
Paredes, Hugo
Barthes, Jean-Paul
Shen, Weiming
Conference: IEEE International Conference on Computer Supported Cooperative Work in Design
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The research is supported by the Innovative Method special project of the Ministry of Science and Technology (Grant No. 2015IM010300), Key Research Program of Shandong Province (2017GGX10140), the Fundamental Research Funds for the Central Universities (Grant No. 2015020031).
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