Abstract: Aiming at located and partitioned characters on motor v ehicle plates. First, we standardized the size, variance of gray scale and mean value of gray scale of the motor vehicle plate images. Second, according to the characteristic of characters on the plate, we selected three different character istics, and constructed three BP nerve nets (NN) to identify the characters on t he motor vehicle plate. At the same time, for the position differences of charac ters on the plate we constructed and trained four different subnets to recognize characters in every BP NN. Finally, we used weighted summation of out-up of ev ery BP NN to get the final result. Furthermore, before combining the result of e ach BP NN, we calculated part two-rank difference in the characters’ image to secondly classify the similar characters in the shape, and so that we can de crease the wrong identification rate. According to the analysis based on the results of the experiments, it is demonstrated that the algorithm has a perfect identification effect.

Key words: pattern recognition, character recognition, neural net work, BP algorithm