Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 1


Published
on

December 15, 2021


Pages

103-112


DOI

Article

Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

Check for updates


Authors

Yerong Zhong Affiliation:
Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd, Qingyuan, Guangdong, China
, Guoheng Ruan Affiliation:
Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd, Qingyuan, Guangdong, China
, Ehab Abozinadah Affiliation:
Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
and Jiaming Jiang Affiliation:
Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd, Qingyuan, Guangdong, China


Abstract

This article proposes a nameplate recognition method based on the least-squares method and deep learning algorithm character feature fusion. This method extracts the histogram of the edge direction of the character and constructs the histogram feature vector based on the wavelet transform deep learning algorithm. We use classifier training for the text recognition of the nameplate to segment the text into individual characters. Then, we extract the character features to build a template. Experiments prove that the algorithm meets the practical application needs of nameplate identification of power equipment and achieves the design goals.


Keywords

deep learning, least-squares method, nameplate recognition, power equipment, 92B20


Citation

Zhong, Y., Ruan, G., Abozinadah, E., & Jiang, J. (2021). Least-squares method and deep learning in the identification and analysis of name-plates of power equipment. Applied Mathematics and Nonlinear Sciences, 6(1), 103–112. https://doi.org/10.2478/amns.2021.1.00055
11 Total citations
1.17 FWCI
2 Recent citations
(2 years)
11 References
Open Access Yes
View full metrics

Published by: Engineering Journals

Engineering Journals Logo