Particle Swarm Optimization-Based Convolutional Neural Network for Handwritten Chinese Character Recognition

Recently, handwritten Chinese character recognition has become an important research field in computer vision. With the development of deep learning, convolutional neural networks (CNNs) have demonstrated excellent performance in computer vision. However, CNNs are typically designed manually, which requires extensive experience and may lead to redundant computations. To solve these problems, in this study, the particle swarm optimization approach is incorporated into the design of a CNN for handwritten Chinese character recognition, reducing redundant computations in the network. In this approach, each network architecture is represented by a particle, and the optimal network architecture is determined by continuously updating the particles until a global particle is identified. The experimental validation resulted in a network accuracy of 97.24% with only 1.43 million network parameters. Therefore, it is demonstrated that the proposed particle swarm optimization method can quickly and accurately find the optimal network architecture.