| Title |
RBFNNs-based Recognition System of Vehicle License Plate Using Distortion Correction and Local Binarization |
| Authors |
김선환(Kim, Sun-Hwan) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2016.65.9.1531 |
| Keywords |
License plate recognition ; Canny algorithm ; Local binarization ; Image warping ; RBFNNs ; PCA ; PSO |
| Abstract |
In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system. |