Optical Character Recognition of Power Equipment Nameplate for Energy Systems Based on Recurrent Neural Network

Abstract

To address the problems of poor accuracy and response time of optical character recognition of power equipment nameplates for energy systems, which are ascribed to exposure to natural light and rainy weather, this paper proposes an optical character recognition algorithm for nameplates of power equipment that integrates recurrent neural network theory and algorithms with complex environments. The collected image power equipment nameplates are preprocessed via graying and binarization in order to enhance the contrast among features of the power equipment nameplates and thus reduce the difficulty of positioning. This innovation facilitates the application of image recognition processing algorithms in power equipment nameplate positioning, character segmentation, and character recognition operations. Following segmentation of the power equipment nameplate and normalization thereof, the characters obtained are unified according to size, and then used as the input of the recurrent neural network (RNN); meanwhile, corresponding Chinese characters, numbers and alphabetic characters are used as the output. The text data recognition system model is realized via the trained RNN network, and is verified by inputting a large dataset into training. Compared with existing text data recognition systems, the algorithm proposed in this paper achieves a Chinese character recognition accuracy of 99.90%, an alphabetic and numeric character recognition accuracy of 99.30%, and a single image recognition speed of 2.15 ms.