## 3 Proposed Methodology
### License Plate Character Segmentation  
Figure 2 shows the complete process of the license plate characters segmentation. As discussed above, we used part-based approach to accomplish this task. The first objective is to segment the region of interest (ROI) in order to reduce the image processing area and to discard a lot of redundant information. In the second part, a proposed character height estimation filter along with connected component analysis (CCA) is applied to extract the required objects. These blocks are discussed below in detail. LP image detected by [1] is fed to part-I for the foreground and background classification. To distinguish the background from the foreground is one of the most difficult and important steps for object segmentation. The proposed method strongly requires the identification of background/foreground color and polarity, as further segmentation processes based on this step. The term polarity is referred to bright or dark based on the intensity of foreground/background colors. We observed that most of the countries use unique color for background as well as for foreground. Probably, the largest color candidates would belong to background region while foreground would have second highest color candidates, which is the key point that we use for background and foreground classification. We propose a model by using RGB color space which is best suited to identifying any color even under various illumination conditions. RGB-based color space can be visualized as a cube with corners of black, the three primaries (red, green, blue), the three secondaries (cyan, magenta, yellow), and white. The eight pure color corners and distribution cube is shown in Figure 3[36]. By considering above mentioned colors and color distribution, a model is proposed that represents all shades of colors into 8 pure colors and has the capability to determine any background and foreground color, which is strongly required in case of multinational VLPs. The total candidates of red, green, blue, yellow, cyan and magenta are computed  
Figure 2: Proposed architecture for license plate character segmentation. by (1) as per the color's definition mentioned in Table 1, and the threshold value is determined by using color distribution cube and rigorous experiments on 3718 images in the test dataset. \[C_{\overline{C}}=\sum_{m=0}^{height-1}\left\{\sum_{n=0}^{width-1}T(I(x_{n}\,\ y_{m}))\right\} \tag{1}\]  
\(T(\zeta)=1\) _when \(\zeta\) is \(T_{\overline{C}}\), where \(\zeta=I(x_{n}\,\ y_{m})\)_  
By using (1) we can get color candidates count (\(C_{\overline{C}}\)) of red, green, blue, cyan, magenta and yellow, but we cannot set any hard limit to separate the group of black and white pixels as the illumination level is unknown. The values of white and black pixels (\(V_{BW}\)) are extracted together as one group and the candidate's vector (\(C_{\overline{BW}}\)) is obtained as  
\[C_{\overline{BW}}=\left\{ \begin{array}{cc}V_{BW}\ if\ V_{BW}\ \ T_{\overline{C}}\\ 0\ \ if\ V_{BW}\ \
otin\ T_{\overline{C}}\end{array} \right. \tag{2}\]  
where \(T_{\overline{C}}=\{d_{1}<0.2\ \&\ d_{2}<0.2\}\). Then [PERSON]'s method based on two intra-class variance is applied to separate them into two classes having black and white pixels separately as shown in Figure 4. Further, larger and smaller class between these two groups is determined as