## 1 Introduction  
License plate (LP) character information is uniquely assigned so that each vehicle on the road can be identified. Therefore, it is widely used for vehicle recognition in situations such as toll charges on highways, speed and signal violations, and illegal parking detection [1; 2; 3]. Due to the serious number of traffic-related problems caused by the rapid increase in vehicles, research is currently underway to improve the traffic environment and LP information is used as an important source of information [4]. For this reason, the study of Automatic LP Recognition (ALPR) has been underway for a long time and is continuing to this day. License plate character recognition (LPCR) is a technology for reading vehicle registration plate character information using optical character recognition. In the conventional LPCR process, each character is segmented from the LP image and the character recognition is performed for individual characters. The end-to-end LPCR method using an object detector based on convolutional neural network (CNN) can recognize LP character information by performing character segmentation and character recognition simultaneously from the LP image. However, this method requires a significantly large number of LP images and their annotations of character information for network training. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. In the following situation, as an example, it is necessary to develop an LPCR module of the ALPR system for Korean LPs. After determining a method and an algorithm for developing the LPCR, a sufficient sample of real LP images is needed for development and testing. If machine learning or deep learning in the algorithm is decided to use for the LPCR, more LP image data are needed, but unfortunately, it is extremely difficult to obtain enough real LP image data. Since it takes a lot of time, effort, and money to obtain enough LP image data to develop a practical LPCR module, we can instead search for public LP datasets. Table 1 shows the available image datasets related to LP from Caltech Cars dataset [5] released from 1999 to the present. Most of the datasets have hundreds of images and UFPR-ALPR [6] dataset offers 4500 images. However, these LP images are not suitable for training LPCR of Korean LPs that consist of numbers and Korean characters, because these datasets are composed of numbers and English or Chinese characters. Thus, Korean LP images had to be collected through web-scraping, but only a few hundred were available. To solve this problem concerning insufficient data, we propose the generation of LP images using a Generative Adversarial Network (GAN)-based image-to-image translation (LP-GAN) model. The advantages of generating LP images using LP-GAN are (1) they look similar to real LP images and (2) they can be used as training data to improve the performance of the LPCR in the ALPR system. The main contribution is three-fold. First, an LP generator based on GAN was made by a small set of real LP images, and generating realistic LP images that have the desired character information for use as training data for the LPCR module of the ALPR system. LP-GAN can be trained using an extremely limited amount of LP image data and can then generate realistic LP images. The generated LP images were used as training data for the LPCR, and were confirmed through experiments that character information could be effectively recognized from a real LP image. In addition, it is shown that the performance of the LPCR could be improved by an ensemble of LP images generated by various LP-GAN generators rather than a single one. Figure 1 shows sample LP images generated by various GAN methods and real LP images. LP-GAN can easily generate LP images of any character combinations, and the generated images look very real.