## 5 Experimental Section
### Experimental Results  
The 12 LPCR modules were trained using 12 datasets among the 13 datasets configured previously (except for Test_13k). The accuracy of the trained LPCR modules was compared using the Test_13k dataset. The LPCR performance of each of the seven characters in a Korean LP was compared and then overall performance from complete LPs was compared. Since the character information of the LP is unique, each item of character information in the LP should be correctly recognized. Therefore, the performance of the LPCR module was evaluated based on the accurate recognition of all seven characters rather than the recognition performance of individual characters. The modified YOLOv2 detector proposed in this paper simultaneously performs the location and classification of the object of interest (i.e., the characters in the LP), but the LPCR module obtains the same result if the character classification is correct, even if the location of the character is incorrect. Hence, the experiments in this study did not consider the locational accuracy of the characters. Table 4 reports the performance comparison results of the LPCR modules trained with the 12 different training datasets. All characters except for the third Korean character are number classes, and the recognition performance of numbers was over 99% with almost all of the comparison datasets. Since there are only 10 classes for numbers and the distinction between the types is obvious, the performance was high regardless of the dataset. In the case of the third Korean character, there are 35 classes, which makes identifying them more complex than for numbers, so there was a difference in performance per dataset. Therefore, the overall performance of LPCR strongly depends on the recognition performance for the Korean characters. The accuracies of the Real_9k, Real_3k, and Real_159 datasets with the proposed modified YOLOv2 detector were 99.78%, 99.72%, and 97.85%, respectively, reflecting high LPCR performance because the LPCR modules had been trained with real LP images and so more easily recognized. This means that the proposed modified YOLOv2 detector gave the LPCR sufficient recognition performance for use in the ALPR system. Provided that there are enough LP images to train the LPCR module, a high-performance LPCR module for the ALPR system could be developed with more than 99.7% overall accuracy. The comparison results of the LPCR performance of the six LPCR modules trained with the six LP datasets generated by the three proposed LP-GAN generators are shown in Figure 7. As can be seen, the performance of the LPCR module trained with the LP images generated by the pix2 pix_cGAN-based LP-GAN was higher than the other two LPCR modules trained with CycleGAN or StarGAN. This means that among the three GAN-based image-to-image translation methods, pix2 pix_cGAN generated LP images that were more realistic than those from the other two methods. For the LP images generated via a single LP-GAN generator, the more items in the training data, the higher the recognition performance. As the last thing to focus on, the overall recognition performance with the Ensemble_3k dataset was 95.56%, which is lower than 96.33% for pix2 pix_cGAN_9k but higher than 93.59% for CycleGAN_9k and 94.23% for StarGAN_9k. Despite the number of training data items being three times higher, higher recognition performance was achieved when training with the 3000 training data items combining the LP images generated from multiple LP-GANs than training with a total of 9000 training data items generated from the single LP-GANs. The overall recognition performance was 98.72% for the Ensemble_9k dataset with the 9000 combined data items, which is almost the same as when trained with real images. This shows that higher performance LPCR modules can be trained with LP images generated by multiple LP-GAN generators. Moreover, it is shown that the proposed LP-GAN can be fully used as a training data generator for the LPCR module of an ALPR system.