License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

Siti Norul Huda Sheikh Abdullah (Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia), Muhammad Nuruddin Sudin (Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia), Anton Satria Prabuwono (Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia) and Teddy Mantoro (Advanced Informatics School, Universiti Teknologi Malaysia, Malaysia)
Copyright: © 2012 |Pages: 25
DOI: 10.4018/jitr.2012070103
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Abstract

For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.
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Introduction

Image detection is one of the crucial issues in image processing besides feature extraction and recognition (Bataineh, Abdullah, & Omar, 2011, 2012). Computer surveillance such as license plate recognition (Romero, Prabuwono, & Taufik, 2011) is an example of object detection application. To ensure that the selected and determined objects were correctly obtained, a very strategic way for object detection is highly required (Abdullah, PirahanSiah, Abidin, & Sahran, 2010; Prabuwono & Idris, 2008). Several prominent ways License Plate Detection (LPD) were for contour detection (Han, Han, Wang, & Zhai, 2003), Hough transform (Soh, Chun, & Yoon, 1994), Radon transform (Shapiro, Gluhchev, & Dimov, 2006), Morphology (Xu & Zhu, 2007), clustering (Siah, 2000; Abdullah, Khalid, Yusof, & Omar, 2007; Abdullah et al., 2010; PirahanSiah, Abdullah, & Sahran, 2011; Abidin, Abdullah, Sahran, & PirahanSiah, 2011) and Speed Up Robust Features (SURF) (Bay, Tuytelaars, & Van Gool, 2006). However, this paper will elaborate on the theories and applications of only some of the mentioned techniques for object detection. Later, these techniques are experimented and compared.

The rest of the paper is organized as follows. Related works and proposed method are discussed in subsequent sections. Then, we justify our proposed work in the Results and Analysis section. This paper concludes our findings in the Conclusion section.

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