An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2%. The experiment results prove that the improved character recognition method is accurate and highly consistent.