Handwritten Chinese Characters Recognition Based on PSO Neural Networks

In order to eliminate the shortcomings of traditional neural networks in handwritten Chinese characters recognition, such as the premature convergence, a novel intelligent method is presented, which uses the particle swarm optimization (PSO) algorithm with adaptive inertia weight to train the neural networks. The main idea is that the optimum weights and thresholds of the neural networks is acquired by the iteration and updating of the swarms, in this process, the inertia weight of the swarm iteration is improved to be adaptive in this paper. In the experimentation, the quantity and distribution information of the strokes of the Chinese character is extracted as the features, then the Chinese characters is classified by the improved PSO neural networks based on these features. Comparing with the BP neural networks, the improved PSO neural networks can avoid the premature convergence and achieve higher precision, in handwritten Chinese characters recognition, the application effect is very notable.