BP neural network has been successfully applied in many fields

such as license plate recognition

however the general method of BP neural network is likely to fall into local optimum rather than converge to the global optimum

so its performance of recognition is not very satisfied. In order to avoid this situation

a license plate number recognition technology method is proposed based on PSO-BP neural network. The key of this method is to build an 8-25-1 BP neural network with 8 pixel rate features as its input vector firstly

and then use particle swarm optimization (PSO) algorithm to optimize the weights and thresholds of the BP neural network to get the best fitness value of global searching. By using 300 pair license plate images some with the different illumination and the stained parts

the simulation result indicates that the algorithm we mentioned comparing with the traditional template matching algorithm and general BP algorithm has the certain application value with the advantage of less output errors

global fast convergence

and high recognition rate.