Efficient character segmentation on car license plates

Lihong Zheng, Junbin Gao, He Xiangjian

Research output: Book chapter/Published conference paperConference paper

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationIEEE International Conference on Control, Automation, Robotics and Vision (ICARCV)
Place of PublicationUSA
PublisherIEEE
Pages1139-1144
Number of pages6
ISBN (Electronic)9781424478149
DOIs
Publication statusPublished - 2010
EventICARCV2010: 11th International Conference - Singapore, Singapore
Duration: 07 Dec 201010 Dec 2010

Conference

ConferenceICARCV2010: 11th International Conference
CountrySingapore
Period07/12/1010/12/10

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  • Cite this

    Zheng, L., Gao, J., & Xiangjian, H. (2010). Efficient character segmentation on car license plates. In IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1139-1144). IEEE. https://doi.org/10.1109/ICARCV.2010.5707938