License Plate Localization and Character Segmentation with feedback self-learning and hybrid-binarization techniques

License Plate Localization (LPL) and Character Segmentation (CS) play key roles in License Plate Recognition System (LPRS). In this study, we dedicate ourselves in these two issues. In LPL, the histogram equalization is employed to solve the low contrast and dynamic range problem; the texture properties, e.g., aspect ratio, and color similarity are used to locate the License Plate (LP). In CS, the hybrid- binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.