## 1 Introduction  
With the rapid advancement of computer technology, deep learning has achieved remarkable progress, finding extensive applications across diverse fields and significantly augmenting the efficiency of related domains. Particularly in the realm of image recognition, the integration of deep learning models has substantially enhanced the comprehension and capability of processing images, thereby streamlining image recognition tasks [1]. In the field of license plate recognition, researchers have gradually begun to focus on the integration of deep learning technology. In-depth research on the integration of deep learning with end-to-end approaches has become a focus of research. End-to-end license plate recognition methods can automatically extract relevant features from input license plate images, reducing manual intervention. This not only helps address challenges associated with slow recognition speeds but also overcomes the complexity of algorithmic processes in traditional methods. In order to improve recognition accuracy, scholars in this field have conducted comprehensive research work. [PERSON] et al. [2] proposed a two-stage Automatic License Plate Recognition (ALPR) framework. In order to improve the reasoning speed, the correction of license plate images and recurrent neural network, which is difficult to calculate in parallel, are abandoned, and Multi-scale Recognition Network (MRNet), a lightweight recognition network based on multi-scale features, is used for license plate recognition of video images, which can be carried out when vehicles are driving at a higher speed. [PERSON] et al. [3] propose a period-based queue length estimation method for isolated signalized intersections based on License Plate Recognition (LPR) data. An improved interpolation method is used to infer the travel time of unmatched vehicles, and the complete arrival and departure information is processed. The three characteristic parameters of the license plates are used as input to the maximum probability function for estimating the queue length on each lane. Thus, the efficiency and accuracy of the detector to locate the license plate are improved. [PERSON] et al. [4] presented an improved vehicle identification approach combining dual license plate recognition and visual features encoding. They introduced two new datasets: UC3M License Plate (UC3M-LP) for license plate detection and character recognition, and UC3M Vehicle Re-Identification (UC3M-VRI) for vehicle re-identification. Their dual system enhances vehicle recognition robustness against image variability. Performance was validated on public and new datasets using a multi-network architecture, achieving notable results. [PERSON] et al. [5] proposed a model for detecting and recognizing license plates in a single retransmission, and adopted a nonlinear loss function to fit the detection process of license plates. In order to reduce information loss, they added ground-truth intersection to Intersection over Union (IoU) to obtain balanced IoU loss. The combination of these two loss functions can make the model obtain better prediction results and improve the accuracy of license plate detection. License plate recognition in complex scenarios plays a pivotal role in practical traffic applications, demanding heightened attention. While enhancing the accuracy of license plate recognition remains a fundamental objective, real-world applications frequently involve intricate situations. Consequently, it becomes imperative to augment license plate recognition in such complex environments. In order to improve the recognition accuracy of license plate images in complex realistic scenes, [PERSON] et al. [6] proposed the method of Faster Region-based Convolutional Neural Network (RCNN) combined with morphological operation to locate the LP region in the detected vehicle to solve the problems of irregular contours, angle change, and occlusion of the license plate, which can meet the requirements of real-time license plate recognition in realistic scenes.