# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the AOLP Dataset  
The application-oriented license plate (AOLP) [35] database consists of 2049 images of a Taiwan license plate. This dataset is categorized into three subsets according to complexity levels and photographing conditions, which are access control (AC), traffic law enforcement (TLE), and road patrol (RP). For the readers' information, below we briefly describe the categories contained in the AOLP dataset. **Access Control (AC):** The AC refers to the cases in which a vehicle passes a fixed passage at a reduced speed or with a full stop, such as at a toll station or the entrance/exit of a region. **Traffic Law Enforcement (TLE):** The TLE refers to cases where a vehicle travels at a regular or higher speed but violates traffic laws, such as a traffic signal or speed limit, and is captured by a roadside camera. Here, 757 images were collected for this application category. **Road Patrol (RP):** The RP refers to the cases where the camera is installed or handheld on a patrolling vehicle and takes images of the vehicles from arbitrary viewpoints and distances. Since we do not have any other images with Taiwan license plates, we use any two of these subsets for training and the remaining one for testing, similar to previous practices. Figure 6 shows our obtained results on the AOLP dataset. Figure 5: Mean LP recognition comparison on the PKU dataset. Figure 6 shows the license plate recognition results for each of the aforedescribed categories of the AOLP dataset. The proposed LPR method works well for scenarios where half of the vehicle bonnet is visible along with the license plate location, which is much lower on the horizontal axis. In each of the images in Figure 6a for the AC category, the proposed method accurately identifies the license plates. Similarly, for the TLE category, as shown in Figure 6b, where the license plates appear in the angular view, the proposed method accurately handles this angle variation by correctly identifying all the license plates. The third image in Figure 6b is especially interesting, as here a yellow vehicle appears at the back side of the license plate, which ultimately results in the partial occlusion of the license plate. Although it does not affect the digits of the plate area, the proposed LPR algorithm handles this partial occlusion and accurately identifies the license plate. Figure 6c shows the RP conditions. Clearly, this is a challenging category as there appears to be a large angle deviation of the viewpoint of the license plate, which makes this scenario challenging for most of the machine learning algorithms. However, as can be seen in Figure 6c, the proposed LPR method reliably handles this issue by indicating the correct number on the license plate. Table 4 lists the LP recognition rate on different classes of the AOLP dataset for works developed in [36; 37; 38; 39]. 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. Table 4 also lists the comparison of the proposed LPR with a few recent methods on the AOLP dataset. As can be seen in Table 4, for the AC category, the proposed method yields the highest recognition rate of the license plates in this category. Figure 6: The LPR on the AOLP dataset is organized under (**a**) Access control, (**b**) Traffic law enforcement, and (**c**). Road patrol conditions. Moreover, in the AC category, the work proposed in [39] ranks second among the compared methods. Similarly, for the TLE category, the proposed LPR method ranks second on the AOLP dataset. In this category, the work reported in [39] yields the highest recognition accuracy. However, the work in [36] ranks fourth among all compared methods, yielding slightly over 94% identification accuracy.