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.
The fast recognition of vehicle license plate based on the improved template matching