# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the CCPD Dataset
The fourth image in the first row of Figure 8 has a relative combination of dark and bright contrast. Overall, the proposed method performs well in the CCPD-rotate category and, as seen in Table 5, produces encouraging results. The second row in Figure 8 shows the license plate identification resultant images from the CCPD-tilt category. The first image in the third row of Figure 8 is a low-contrast image example that has severe black contrast. It can be seen that our developed method is unaffected by this situation and accurately identifies the license plate. Similarly, the last image in the third row of Figure 8, which has a slightly misplaced license plate, is highly challenging in the tilt category. However, our developed method also handles this case intelligently and produces accurate output. More output resultant images from the CCPD dataset are shown in the third row of Figure 8, where a few cases are shown for the different weather conditions. The first three images in the third row of Figure 8 correspond to the snowy weather where our developed method reports accurate results, whereas the fourth image is for the rainy day in which our developed method performs at par and yields accurate results. The fourth row in Figure 8 is for the outputs generated by the algorithm for the CCPD challenge category. During simulations, we find that this is the most challenging category in the dataset, and it is not easy for every algorithm to handle this. The first image shown in the last row of Figure 8 indicates that both the vehicles and the outside environment are severely dark. However, our developed method handles this scenario and yields accurate recognition results. The same is true for the third image in the last row, where our approach accurately identifies and identifies the license plate. Similarly, for the 2 nd image in the last row of Figure 8, there appears to be a shadow on the road and the vehicle, and there is also a bright light in the center of the license plate. However, our developed method passes through this hurdle and yields the correct result. Likewise, the rightmost bottom image in Figure 8 is the case where there are back lights turned on, and half of the license plate has a blue background with white color text on it while the other half has a light grey background with yellow text over it. Consequently, our established approach delivers accurate and encouraging results in this case. Table 5 lists the LP recognition rate on different classes of the CCPD dataset for works developed in [40; 41; 42; 43; 44]. It is important to state here that these methods were chosen for comparison on the AOLP dataset because their evaluations on this dataset, along with standard implementation, are publicly available. This makes a fair reason for us to train these methods on the AOLP dataset along with our developed method. In Table 5, we show the comparison of our developed method with these five techniques on the CCPD dataset for all the categories. It can be seen that, for the CCPD-Base category, our method ranks second out of all the compared methods. In this category, the work reported by [44] has the highest accuracy. In this category, the work conducted in [40] has the least recognition accuracy. For the CCPD-DB category, our method follows [44] and lies in the second position. Here, the work in [41] has the lowest accuracy. For the CCPD-FN category, our method and [44] have the highest license plate recognition accuracy, followed by the work done in [42]. For the CCPD-rotate category, our developed technique beats the compared works and yields the highest identification accuracy of license plates. In this category, the work done in [43] yields the lowest identification rates. Moreover, for the CCPD-Tilt category, our method has the highest recognition accuracy, followed by the work in [40], which has the lowest reported license plate identification rate. For the CCPD-weather category, our method again beats the compared works. Here, the work in [40] has the lowest recognition rates.