A novel license plate character segmentation method for different types of vehicle license plates

Md. Mostafa Kamal Sarker; Moon Kyou Song

doi:10.1109/ICTC.2014.6983089

A novel license plate character segmentation method for different types of vehicle license plates

Md. Mostafa Kamal Sarker, Moon Kyou Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods.

Original language	Undefined/Unknown
Title of host publication	2014 International Conference on Information and Communication Technology Convergence (ICTC): Proceedings
Number of pages	5
ISBN (Electronic)	978-1-4799-6786-5
DOIs	https://doi.org/10.1109/ICTC.2014.6983089
Publication status	Published - 15 Dec 2014
Externally published	Yes

Publication series

Name	International Conference on Information and Communication Technology Convergence (ICTC)
Publisher	IEEE

Access to Document

10.1109/ICTC.2014.6983089Licence: Unspecified

http://www.scopus.com/inward/record.url?eid=2-s2.0-84920108835&partnerID=MN8TOARS

Cite this

Sarker, M. M. K., & Song, M. K. (2014). A novel license plate character segmentation method for different types of vehicle license plates. In 2014 International Conference on Information and Communication Technology Convergence (ICTC): Proceedings (International Conference on Information and Communication Technology Convergence (ICTC)). https://doi.org/10.1109/ICTC.2014.6983089

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abstract = "License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods.",

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A novel license plate character segmentation method for different types of vehicle license plates. / Sarker, Md. Mostafa Kamal; Song, Moon Kyou.

2014 International Conference on Information and Communication Technology Convergence (ICTC): Proceedings. 2014. (International Conference on Information and Communication Technology Convergence (ICTC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods.

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