Efficient character segmentation on car license plates
In this paper an improved hill climbing algorithm based method is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of two different types of image segmentation approaches, the hill climbing algorithm based method gave a better image segmentation results. The hill climbing algorithm was modified by introducing automatic parameter determination and smart searching. After modification it efficiently detects the peaks (local maxima) that represent different clusters in the global histogram of an image. The process is successful by getting a clean license plate image removing all unwanted areas. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate after non-character areas are removed. The recognition rate increased from about 30.6% before our proposed process to about 91.3% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved.