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Proceedings Paper

Design of chip character recognition system based on neural network
Author(s): Yutong Hu; Peng Wang; Jun Wu; Tingting Xu; Jiahan Wang
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Paper Abstract

At present, the sample comparison of intelligent electric meters in power grid companies mainly relies on manual inspection. With the development of semiconductor technology and the increasing demand of intelligent electric meters, the disadvantages of this method, such as low detection efficiency, high misjudgment rate, are becoming more prominent. In this paper, a method for automatically detecting and identifying the intelligent electric meter circuit board chip is proposed, and a chip character recognition system based on convolutional neural network (CNN) is designed. The system is mainly divided into two parts: chip positioning and character recognition. The chip is positioned based on the method of layout analysis and edge detection. According to the difference between the characteristics of the chip and the characteristics of the PCB background, preprocess the images, detect the chip identification and obtain multiple candidate regions. Finally, candidate regions are screened based on chip characteristics. The gray-level projection method is used to segment characters. A single character image is obtained by row segmentation and column segmentation. At the same time, the optimization algorithm for character adhesion and fracture problem is proposed to improve the segmentation accuracy. For the character recognition module, build a convolutional neural network to extract character features, and the normalized character is input into the trained neural network for recognition. The recognition accuracy of test sets is high, and the time for recognizing a single character is about 0.35 seconds. Compared with the traditional detection methods, the proposed method has higher detection efficiency and recognition accuracy.

Paper Details

Date Published: 12 March 2020
PDF: 11 pages
Proc. SPIE 11439, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 1143908 (12 March 2020); doi: 10.1117/12.2541127
Show Author Affiliations
Yutong Hu, Tianjin Univ. (China)
Peng Wang, Tianjin Univ. (China)
Jun Wu, State Grid Liaoning Electric Power Co., Ltd. (China)
Tingting Xu, State Grid Liaoning Electric Power Co., Ltd. (China)
Jiahan Wang, State Grid Liaoning Electric Power Co., Ltd. (China)


Published in SPIE Proceedings Vol. 11439:
2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems
Jigui Zhu; Kexin Xu; Hai Xiao; Sen Han, Editor(s)

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