## 5 Results  
First of all, the execution times were examined for the original network discussed in Section 3 on two different types of GPU environments: on a desktop workstation equipped with a Quadro K2200 GPU card and a powerful Jetson TX1 embedded board. The Quadro K2200 and the Jetson TX1 have 640 and 256 CUDA cores, respectively. The execution time of these torch-based networks was measured for the performance analysis of the original network. An input image size of 640 \(\times\) 480 was selected for multi-scale classifications of license plates. As shown in Table 1, there are two scenarios in the original  
Figure 8: Output image. Figure 7: Classifications of overlapping slots and selection of characters for the final result. plate recognition system. If a plate is not detected in the frame, then the remaining steps of plate and character classification would not be performed, as depicted in Figure 3. This significantly reduces the computation time, as can be seen in Table 1. Conversely, if a plate is detected, then all processing blocks would be executed to find the final results that include processes like the merging of results and reclassifications. As mentioned earlier, the original plate recognition system is highly precise and has an accuracy of 98% on real photos. However, it can be observed that the execution time of the original network on an embedded platform like Jetson TX1 board was higher (\(1.45+3.24=4.69\) s) in the case of positive classification. The results are listed in Table 1.  
This execution time can be much higher on the mobile platforms that are usually less powerful than embedded boards and desktop workstations. By simplifying the original flow and eliminating the computationally intensive tasks such as merging results over and multi-scale classifications, this execution time can be reduced significantly at the expense of some classification accuracy. The simplified flow proposed in Section 4 was first evaluated on Quadro K2200 and Nvidia Jetson TX1 embedded boards for comparison of performance with the original network in terms of execution time. Results show that the simplified flow significantly improved the execution time. Then Nvidia Shield K1 tablet was used to measure the performance. This mobile device has a Tegra K1 GPU with 192 CUDA cores. The execution time of the plate detector and localizer was approximately 300 milliseconds for an input image of 128 \(\times\) 64. Additionally, the character detector classified a cropped license plate of size 188 \(\times\) 40 in around 250 milliseconds. Results show that by using input images of smaller dimension and single-scale classification, the execution time of an automatic license recognition system can be decreased. Furthermore, an embedded platform like a mobile device has a limited storage capacity; thus, the size of the used framework is also an important constraint to consider. In the case of deep classifiers, enough memory space is required to store the trained parameters, the used framework (torch), and the required computational packages. By using a CUDA-only replicated network, there is no need to install extra computational packages. Furthermore, by converting the trained parameters to float data type, the size of all parameters is reduced by half. Table 2 lists the size of the trained imported from Torch computing framework and converted parameters for CUDA-based simplified flow. However, the accuracy of this simplified architecture was indeed decreased. Classification accuracy of a single character detection and full plate detection (all seven characters) is listed in Table 3 for different offsets. The testing dataset consists of 788 crops of aspect ratio 2:1 roughly centered around Italian rear license plates. These are real-world images downloaded from the internet ([[http://platesmania.com/--the](http://platesmania.com/--the)]([http://platesmania.com/--the](http://platesmania.com/--the)) database is available as supplementary material) and are publicly available.