Performance comparison between SVM-based and RBF-based for detection of saudi license plate

License plate recognition (LPR) systems utilized classifier to recognize the license plate image. In addition, several license plate detection method are also demanded a classifier to differentiate between plate and non-plate. Currently, there are two types of LPR, which are fixed and mobile LPR. The detection process becomes crucial in mobile LPR systems. It is because the license plate can be captured in various angles. The environment also cannot be controlled, and it affects the performance of LPR systems significantly. Thus, this paper proposed license plate localization for Saudi vehicle based on statistical features and utilized two different classifiers including SVM and RBF. The proposed method is tested on collected database consist of Saudi vehicles. Finally, a comparison is carried out to evaluate performance of SVM and RBF. Experimental result shows proposed method for license plate detection using RBF-based outperformed SVM-based.