# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the CCPD Dataset  
The CCPD dataset [40] is the largest publicly available LP dataset and has a collection of over 290,000 Chinese LP images. This dataset is separated into several categories according to the difficulty of identification, for instance, the illuminations on the LP area, the distance from the license plate when photographing, and the degree of horizontal and vertical tilts. The CCPD dataset also contains images in different weather conditions, such as rainy, snowy, or foggy. Each category includes 10,000 to 20,000 images. The CCPD-base consists of approximately 200,000 images, of which 100,000 are used for training and the other half are for testing. As listed in Table 5, the other sub-datasets, such as the CCPD-DB, the CCPD-FN, the CCPD-rotate, the CCPD-weather, and the CCPD-challenge, are also used during the test phase. Figure 7a shows a few samples of the output images on the CCPD-base images. Clearly, the proposed method performs well on all images. Particularly, the left-most image has huge illumination variations with very limited visible contrast in the license plate area. The proposed method handles that scenario well and correctly identifies the license plate. Similarly, the second, third, and fourth images in the top row of Figure 7a are the cases \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**Accuracy \% on Each Category**} \\ \cline{2-5}  & **AC: No of** & **TLE: No of** & **RP: No of** & **Mean** \\  & **Images = 681** & **Images = 757** & **Images = 611** & **Recognition** \\ \hline
[36] & 94.9000 & 94.2000 & 88.4000 & 92.5000 \\
[37] & 95.3000 & 96.6000 & 83.7000 & 91.8666 \\
[38] & 96.6000 & 97.8000 & 91.0000 & 95.1333 \\
[39] & 97.3000 & 98.3000 & 91.9000 & 95.8333 \\ \hline
**Proposed** & **97.8970** & **98.2719** & **91.9006** & **96.0231** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the AOLP dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **CCPD-Base** & **CCPD-DB** & **CCPD-FN** & **CCPD-Rate** & **CCPD-Tilt** & **CCPD-Weather** & **CCPD-Challenge** & **Overall** \\  & **(100 k)** & **(20 k)** & **(20 k)** & **(10 k)** & **(10 k)** & **(10 k)** & **(10 k)** & **(10 k)** & **(10 k)** \\ \hline
[40] & 98.5000 & 96.9000 & 94.3000 & 90.8000 & 92.5000 & 87.9000 & 85.1000 & 95.5000 \\  
where the license plate appears in the angular view. However, our proposed method handles this scenario and identifies all the license plates. Figure 7b shows the CCPD-blur image output of our developed method. Most of these blurred images were captured in outdoor conditions with strong sunlight and complex backgrounds. Since these images appear blurry, the license plate area has a low resolution. However, it can be seen in the second row of Figure 7b that our developed method performs significantly well and identifies all the license plates shown therein in the second row. Particularly, the first and third images in the second row of Figure 7b are indicative of the good performance of our developed method where the background is complex along with various other objects. Moreover, the third row in Figure 7c is the sample output of our proposed method for the CCPD-FN cases. Clearly, in this case, our developed method is quite accurate and reliably identifies all the license plates shown therein. It is to be noted that the third row in Figure 7 also contains complex backgrounds. However, the good performance of our developed method is unaffected by these factors. Figure 7: The LPR on the CCPD dataset for (**a**) base, (**b**) blur, and (**c**) FN scenarios. A more detailed analysis of our developed method is shown in Figure 8. As can be seen, the outputs in the first row of Figure 8 are from the CCPD-rotate category. Particularly, the first image has a rotated license plate along with an overly whitish appearance due to the presence of very strong sunlight. Clearly, the developed method handles such a scenario and accurately identifies the license plate.