# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Discussion  
Although Section 4 sheds detailed light on the validity of the proposed algorithm points. However, discussion below gives further insight about the feasibility of the proposed algorithm.  
* The aforementioned qualitative analysis demonstrates that our proposed license plate detection algorithm outperforms the previous published methods. We observe that such improved performance has been obtained due to precise development of LPLM as described in Algorithms 1 and 2, which exactly handles the image variations as shown in Figure 5a.
* Although our method handles the license plate angle variation as shown in the PKU dataset, we observe that in the PKU dataset the LP angle variation is just the deviation of the image capturing device that results in the view variation. However, to accurately handle the actual angle variations, such as rotated or tilted license plates, estimates of the angle of multi-directions may be required [23].
* Although the proposed algorithm efficiently handles non-uniform illuminations in the PKU dataset, when we test our algorithm on real life images that contain severe dark contrast at different day times and in unconstrained environments [29], a considerable decrease in detection accuracy is observed, as shown in Figure 5a. In such situations, we therefore recommend to use a state-of-the-art image enhancement algorithm to rectify the non-uniform illuminated images [28]. However, our proposed license plate detection algorithm accurately handles certain challenging scenes, for instance, extreme reflected glare as shown in Figure 3b.
* Color variation is an important aspect to be considered during the license plates study. In the PKU dataset, we have successfully detected different color license plates, such as Blue, Yellow, White, and Black, as shown in Figure 5a. We observe that when the vehicle and its license plate have the same color, the proposed algorithm might not yield encouraging results. Moreover, to detect vehicles and to locate license plates when RGB images are used, the HSV image is processed. We empirically observe that either RGB or HSV alone are insufficient to handle the sharp variations that occur in the input images.
* From the total of 3977 images containing 4263 license plates in the PKU database, the proposed algorithm accurately observed the license plates in 3939 images. As shown in Table 3, our proposed algorithm yields an average detection accuracy of 99.04% on this database. Our simulations also reveal that PKU-G5 category still needs considerable attention, on which the proposed algorithm yields 97.30% detection accuracy.
* We believe that the proposed work is an encouraging solution in the real-time detection of targeted license plates. After the successful detection, later, a suitable action can be taken, such as plate area estimation and recognition, vehicle owner identity, and theft/occlusion cases [30; 31; 32; 33; 34]. Similarly, for security applications, any suspicious object/plate can be tracked [35] and estimated [36; 37].
* Since, the ALPD is a preliminary step for a license plate recognition system [38; 39; 40; 41], any license plate recognition system is therefore highly dependent upon accurate and precise license plate detection that will be fed to the recognition system.