Automatic Vehicle Identification by Plate Recognition for Intelligent Transportation System Applications Kaushik Deb 1 , My Ha Le 2 , Byung-Seok Woo 2 , and Kang-Hyun Jo 2 1 Dept. of CSE, Chittagong University of Engineering & Technology, Chittagong-4349, Bangladesh 2 Dept. of EE and Information Systems, University of Ulsan, Daehak road 100, Nam-gu, 680-749 Ulsan, South Korea debkaushik99@cuet.ac.bd, {lemyha, woo}@islab.ulsan.ac.kr, acejo@ulsan.ac.kr Abstract. Automatic vehicle identification is a very crucial and in- evitable task in intelligent traffic systems. In this paper, initially, a Hue- Saturation-Intensity (HSI) color model is adopted to select automatically statistical threshold value for detecting candidate regions. The proposed method focuses are on the implementation of a method to detect candi- date regions when vehicle bodies and license plate (LP) have similar color based on characteristics of color. Tilt correction in horizontal direction by the least square fitting with perpendicular offsets (LSFPO) is pro- posed and implemented for estimating rotation angle of the LP region. Then the whole image is rotated for tilt correction in horizontal direction by this angle. Tilt correction in vertical direction by reorientation of the titled LP candidate through inverse affine transformation is proposed and implemented for removing shear from the LP candidates. Finally, statistical based template matching technique is used for recognition of Korean plate characters. Various LP images are used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness. Keywords: HSI color model, Tilt correction, Least square fitting with perpendicular offsets (LSFPO), Affine transformation, and template matching. 1 Introduction With the rapid development of highway and the wide use of vehicle, people have started to pay more and more attention on the advanced, efficient and accurate intelligent transportation systems (ITSs). The task of recognizing specific object in an image is one of the most difficult topics in the field of computer vision or digital image processing. The vehicle license plate recognition (VLPR) task is quite challenging from vehicle images due to the view point changes, when vehicle bodies and LP have similar color, multi-style plate formats, and the nonuniform K.G. Mehrotra et al. (Eds.): IEA/AIE 2011, Part II, LNAI 6704, pp. 163–172, 2011. c  Springer-Verlag Berlin Heidelberg 2011