Abstract—The detection and recognition of information in natural scenes has always been a difficult problem in computer vision. Digital instrument character recognition is one of the more representative and valuable things. In recent years, there is a lot of research work on this problem, but the solutions rely on string location, character segmentation and other preprocessing processes, the results of these preprocessing processes directly affect the final character recognition results. In contrast, the character recognition method of digital instrument based on convolution neural network (CNN) omits the complex preprocessing process through graph to graph prediction, and the character recognition result is obtaind directly. And has a strong generalization ability, can identify multiple types of instruments. At the same time, through the weighted fusion of muti-scale and multi-level features in the CNN, a better ability of feature extraction and information integration is obtained. The experimental results show that the method can directly and accurately recognize the characters in the Vernier caliper.

Index Terms—Digital recognition, convolution neural network, detection, computer vision.

Hui Sun, Feng Shan, Weiwei Shi, Xiaowei Wang, Xiaofeng Li, Yuanfang Cheng, 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:Hui Sun, Feng Shan, Xiaoyun Tang, Weiwei Shi, Xiaowei Wang, Xiaofeng Li, Yuanfang Cheng, Haiwei Zhang, "Intelligent Digital Recognition System Based on Vernier Caliper," International Journal of Computer and Communication Engineering vol. 10, no. 1, pp. 1-8, 2021.

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