# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the PKU Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{7}{c}{**PKU Dataset Categories**} \\ \hline
**Object** & **Ref** & **G1** & **G2** & **G3** & **G4** & **G5** \\ \hline \multirow{8}{*}{Vehicle} & [32] & 100 & 100 & 100 & 99 & 98.50 \\  & [33] & 99 & 98 & 98.20 & 99.10 & 98 \\  & [34] & 100 & 100 & 99 & 99.10 & 98 \\  & [45] & 100 & 100 & 100 & 98.96 & 99.13 \\  & [46] & 100 & 100 & 100 & 99.72 & 99.10 \\  & [47] & 100 & 100 & 100 & 99.74 & 99.22 \\  & **Proposed** & **100** & **100** & **100** & **99.70** & **99.10** \\ \hline \multirow{8}{*}{License Plate} & [32] & 99 & 97.05 & 98.80 & 99 & 98.50 \\  & [33] & 97 & 98.01 & 98.20 & 99.10 & 98 \\  & [34] & 98.50 & 98.22 & 98.55 & 99.10 & 98 \\  & [45] & 98.80 & 99.45 & 99.15 & 98.96 & 99.13 \\  & [46] & 99.85 & 99.50 & 99.22 & 99.35 & 97.35 \\  & [47] & 99.87 & 99.65 & 99.40 & 99.40 & 97.35 \\  & **Proposed** & **99.81** & **99.50** & **99.20** & **99.40** & **97.30** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Category-wise Vehicle + License Plate detection comparison (%) on PKU dataset. **LP recognition: G3-category:** As shown in the third row in Figure 4, the proposed LPR method reliably handles the high-glare images. The LPs on the vehicles in the first two pictures in this row are clear enough to be correctly identified. Similarly, it is clear from this row that our developed method handles low-contrast images in which both the vehicles and the background have blackish appearances. Generally, it is observed in the third row of Figure 4 that our developed method has barely any effect on its recognition performance with blackish objects against a black background. **LP recognition: G4-category:** As shown in the fourth row in Figure 4, the area around the vehicles is highly dark. There also appear to be glare and high beams from vehicles. However, in all four images shown for this category in Figure 4, our developed method accurately identifies all the LP numbers and successfully handles the glare situations. **LP recognition: G5-category:** As shown in the fifth row in Figure 4, there appear to be multiple vehicles and LPs in the images. For all the images shown, our developed method identifies all the LP that appear in the images. In the second and fourth images, there appear to be three LPs. In the fourth image, our method identifies all three LPs, whereas, in the second image, only two LPs are detected out of three. One reason is the red text that appears in the input image around the LP area, which created a hurdle for our developed method. Table 3 lists the LP recognition rate for each of the PKU categories for works developed in [32; 33; 34]. It is important to state here that these methods were chosen for comparison on the PKU dataset because their standard implementation is publicly available. This makes it logical to train these models on the PKU dataset along with our developed method. From Table 3, it is evident that all the compared methods yield over 90% recognition accuracy for the G1 category. Our developed method yields 100% LP recognition accuracy in this category. The work developed by [PERSON] et al. [34] ranks second, yielding 98% accurate recognition. For the G2 category, the work developed by [PERSON] et al. [33] yields the lowest LP recognition accuracy of 90.50%. In this category, our developed method ranks first, followed by the work reported in [34]. For the G3 category, the works on [33] and [34] yield almost similar results by producing at least 90% recognition accuracy. In this category, the work developed by [PERSON] et al. [32] also reports 92.60% LP recognition. For the G4 category, we observe that works in [32; 33; 34] yield below 90% LP recognition. In this category, our developed method comprehensively outperforms the compared methods. For the G5, which is the most difficult category of the PKU dataset, the work in [32] produces the least accuracy of 72%, followed by [34], whose accuracy is a bit over 80%. In this category, our developed method yields 99.63% recognition accuracy.