Character Segmentationfor License Plate Recognition by K-Means algorithm

Publisher:
Springer-Verlag Berlin / Heidelberg
Publication Type:
Conference Proceeding
Citation:
Image Analysis and Processing - ICIAP2011, 2011, pp. 444 - 453
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011003848OK.pdf495.37 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: