Abstract—Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

Index Terms—Digital instruments, intelligentization, image recognition, convolution neural network.

Feng Shan, Hui Sun, Weiwei Shi, Xiaowei Wang, Xiaofeng Li, Xurong Zhang, Haiwei Zhang are with Technical Department, NO. 5715 Factory of People’s Liberation Army, Luoyang City, Henan Province, China. Xiaoyun Tang is with The First Military Representative Office of the Air Force equipment Department in Luoyang area, Luoyang City, Henan Province, China.

Cite:Feng Shan, Hui Sun, Xiaoyun Tang, Weiwei Shi, Xiaowei Wang, Xiaofeng Li, Xurong Zhang, Haiwei Zhang, "Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network," International Journal of Computer and Communication Engineering vol. 9, no. 4, pp. 185-192, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).