# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the PKU Dataset
Therefore, an accuracy of slightly over 99.75% is very encouraging in the later stages of the algorithm. * Table 2 also lists the LP detection comparisons for several methods. As can be seen, the improved YOLO-v7 [46] ranks first in all five categories of the PKU dataset in terms of LP detection. The standard YOLO-v7 method [45] ranks 2 nd in terms of license plate localization on this dataset. For the G1 and G2 categories, all of the methods compared had a LP detection accuracy of at least 97%, whereas, for the G3 category, the methods listed in Table 2 yielded at least 98% LP detection. For the G4 category, approximately 99% LP detection is achieved. The G5, which is the most challenging category in the PKU dataset, is also addressed nicely. In this category, the methods listed in Table 2 yield at least 98% accurate license plate detection. In addition, our method yields at least 99% LP detection for G1, G2, G3, and G4 categories. In the G5 category,  
Figure 3: Vehicle+LP detection on PKU dataset. our finely tuned version of the Faster RCNN achieves 97.30% accurate license plate detection accuracy. * Our analysis indicates that the mean LP detection accuracies of the works [32; 33; 34; 45; 46; 47] are found to be 98.47%, 98.06%, 98.47%, 99.09%, 99.05%, and 99.13%, respectively. The aforementioned LP detection accuracies are a good indicator that all the compared methods yield at least 98% license plate detection accuracy. YOLO-based methods [45; 46; 47] perform well to locate an object, such as a vehicle or license plate. However, from Table 2, we find that our method, which employs a fine-tuned version of the Faster RCNN, yields a mean license plate detection accuracy of 99.04%. The aforedescribed analysis is a good indicator of the application of the various methods to achieve objects, such as vehicles and license plates, in various real-life applications. Vehicle and license plate detection is a prototype of our developed system. Therefore, our deployed detectors also yields at par results with the recently published works. With the state-of-the-art method listed in Table 2, detection accuracy is almost at par with that of conventional methods. After the objects, which in our case are vehicles and LPs, are located, in the next phase we process the detected LP area for recognition. It is important to state that in the PKU dataset, all the visible license plate labels are not annotated. Therefore, in the PKU dataset, we labeled the 2250 images. The 1355 images are randomly selected for training, and the other 901 are used for testing. To evaluate license plate recognition accuracy, the license plate was localized by a bounding box as shown in Figure 3 for each category of the PKU dataset. The detected license plate is now fed to our newly developed recognition module. As shown in Figure 4, the proposed LP recognition technique correctly understands different LPs that appear in each of the five categories of the PKU dataset. The important points noted during the LP recognition task are discussed further below. **LP recognition: G1-category:** As shown in the first row in Figure 4, the proposed recognition algorithm precisely identifies the LPs shown therein. Our obtained correct recognition result is shown on top of the original LP on the input vehicle images. The third image in the first row of Figure 4 has a relatively complex background. However, it does not pose any threat to the proposed method of achieving the correct identification result. **LP recognition: G2-category:** As shown in the second row in Figure 4, the first three images have different car colors with their own installed LPs. Our method correctly identifies all such cases. However, the fourth image of the bus with visible LP has a relatively complex background. Nevertheless, the proposed method handles this scenario well and achieves the correct result on top of the original LP shown therein.