@inproceedings{13c83e102cb4433cbf89540c62b21365,
title = "Character segmentation for license plate recognition by K-means algorithm",
abstract = "In this paper an improved K-means algorithm 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 different image segmentation approaches, the K-means algorithm based method gave better image segmentation results. The K-means algorithm was modified by introducing automatic cluster number determination by filtering SIFT key points. After modification it efficiently detects the local maxima that represent different clusters in the image. The process is successful by getting a clean license plate image. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate. The recognition rate increased from about 86.6% before our proposed process to about 94.03% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved.",
keywords = "K-means algorithm, LPR, image segmentation",
author = "Lihong Zheng and Xiangjian He",
year = "2011",
doi = "10.1007/978-3-642-24088-1_46",
language = "English",
isbn = "9783642240874",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "444--453",
booktitle = "Image Analysis and Processing, ICIAP 2011 - 16th International Conference, Proceedings",
edition = "PART 2",
note = "16th International Conference on Image Analysis and Processing, ICIAP 2011 ; Conference date: 14-09-2011 Through 16-09-2011",
}