## 1 Introduction  
License plate (LP) identification is a critical process in many areas due to the significant number of vehicles caused by urbanization and related issues such as toll collection, vehicle verification, and speeding detection. Collected data can help identify mobility patterns, travel demand, congestion patterns in cities, traffic management [1], and even estimate emissions [1, 2]. There are various LP recognition and identification algorithms proposed, but they can be divided into two main categories: those using deep learning and traditional algorithms. Images are now collected not only by speed recorders from passing vehicles or people but also by drones, and they are often taken at oblique angles and can be blurry, distorted, etc. Therefore, it is important to improve the quality of the images. One solution based on the phase congruency model (PCM) uses a combination of DCT-PCM (discrete cosine transform PCM) for plate text detection. The proposed method presents a new clustering approach for eight neighboring pixels that aims to avoid background pixels being classified as candidate pixels [3]. This approach helps to extract the edges of the text, and false positives are further eliminated by applying a fully connected neural network. The method achieves competitive results for the total-text dataset when compared to other methods. However, it should be noted that the proposed method was specifically designed to work with both drone images and usual images taken in an orthogonal direction. Other methods may perform better when dealing with specific types of datasets. Another solution used two generative adversarial networks (GANs) to remove noise in the image and increase image resolution [4]. Although computational time is larger compared to similar methods, the final image quality is increased significantly. Deep convolutional neural networks (CNNs) are commonly utilized for various image recognition tasks due to their ability to automatically extract features, making them a powerful tool in medicine, human pose, and face recognition [5; 6; 7; 8]. License plate matching is typically achieved through license plate character registration (LPCR) or license plate feature (LPF) extraction. A novel LPF-based LP recognition method was developed using a deep CNN. This method employs a multitask learning approach that first recognizes parallel letters and then classifies images [9]. The proposed CNN model was designed using a modified VGG (visual geometry group) architecture, which yielded better results compared to existing state-of-the-art methods. However, it should be noted that the VGG model is a large network and may take more time to train, which could be a drawback in situations where real-time license plate identification is required. Another approach that uses a deep neural network, called the capsule network, improves the processing time by integrating the segmentation process and extracting features from the segmented data using the CN framework [10]. Capsule networks are advantageous in preserving detailed information throughout the network. However, routing methods such as EM-routing or routing by agreement have a negative effect on the capsule network, preventing it from distinguishing inputs and their negative counterparts. A two-stage DNN is used to detect license plates on the unedited raw image using YOLOv2 (You Only Look Once), and then cropped images are used to recognize license plates. While it has demonstrated excellent recognition results, the computational time could be improved [11]. A common problem that occurs during the recognition process is data loss being passed from one layer to another. To address this issue, an adversarial network is used to restore lost input data and then passes it to the following network (YOLOv3), which is responsible for finding symbols and can do so in three different feature map sizes: 31, 16, and 8 [12; 13]. YOLOv3 employs scaled anchor boxes, while YOLOv2 uses boxes of the same size.