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, https://doi.org/10.1109/CSCWD.2019.8791876
Deep learning based container text recognition
|Author:||Zhang, Weishan1; Zhu, Liqian1; Xu, Liang2;|
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
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042923152
Institute of Electrical and Electronics Engineers,
|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.
|Pages:||69 - 74|
23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
|Host publication editor:||
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
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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