<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Department of Information System, Faculty of Computing and Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This article proposes a nameplate recognition method based on the least-squares method and deep learning algorithm character feature fusion. This method...

Method	F-value, %
Sundararajan et al. [1]	53.00
Yang et al. [2]	64.00
O’Brien et al. [3]	66.18
CTPN + CRNN	64.34
CTPN + OCR software	42.50
This study	68.69

Size	Number of characters/piece	Detection time, s	Recognition time, s	Total time, s
301 × 472	31	0.341	2.76	3.101
300 × 401	70	0.289	2.1	2.389
450 × 800	83	0.364	2.25	2.614
532 × 710	73	0.141	2.81	2.951
241 × 629	78	0.321	2.66	2.981
3024 × 4032	63	0.542	2.12	2.662

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

Published Online: Dec 15, 2021

Page range: 103 - 112

Received: Jun 16, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.1.00055

Keywords
deep learning, least-squares method, nameplate recognition, power equipment

© 2021 Yerong Zhong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Comparison of experimental results based on the SVT dataset

Comparison of nameplate recognition accuracy

Partial image processing time

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

Published Online: Dec 15, 2021

Page range: 103 - 112

Received: Jun 16, 2021

Accepted: Sep 24, 2021

DOI: https://doi.org/10.2478/amns.2021.1.00055

Keywordsdeep learning, least-squares method, nameplate recognition, power equipment

© 2021 Yerong Zhong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Comparison of experimental results based on the SVT dataset

Comparison of nameplate recognition accuracy

Partial image processing time

Keywords
deep learning, least-squares method, nameplate recognition, power equipment