Deep Learning-Aided OCR Techniques for Chinese Uppercase Characters in the Application of Internet of Things

Optical character recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain digital information from various images on the Internet of Things (IoT). However, existing OCR techniques pose a big challenge in the recognition of the Chinese uppercase characters due to their poor performance. In order to solve the problem, this paper proposes a deep learning-aided OCR technique for improving recognition accuracy. First, we generate a database of the Chinese uppercase characters to train four neural networks: a convolution neural network (CNN), a visual geometry group, a capsule network, and a residual network. Second, the four networks are tested on the generated dataset in terms of accuracy, network weight, and test time. Finally, in order to reduce test time and save computational resources, we also develop a lightweight CNN method to prune the network weight by 96.5% while reducing accuracy by no more than 1.26%.