## 2 Related Work  
This section presents several articles focused on the recognition of characters within vehicle license plates, as well as research that develops bio-inspired retinal models applied in various disciplines and their impact on automated systems. The recognition of letters and numbers on vehicle license plates can be divided into three different stages:  
1. Detection and extraction of the region of interest containing the license plate, where the effort is focused on detecting vehicle license plates in a scene and ensuring that they are of readable quality. 2. Number and letter plate character segmentation, where the effort is focused on determining which part of a vehicle license plate segmented from the scene contains numbers and letters. 3. Recognition of alphanumeric characters, where, after determining the location of patterns on a vehicle license plate, two questions are answered: What letters are present on a vehicle license plate? What numbers are on a vehicle license plate? The answers must be presented in logical order [2]. Regardless of the stage, the use of deep learning-based methods has improved accuracy and the ability to recognize vehicle license plates in challenging environments. Convolutional neural networks (CNNs) have shown superior performance in this area. However, one of the main challenges associated with deep learning techniques is the need for large quantities of data, as the variability and quantity of training data influence performance [3]. The authors also highlight two main challenges in vehicle license plate detection (LPD). First, existing methods often use horizontal rectangles to enclose objects, which may not be suitable for license plates with different orientations due to rotation and perspective distortion. Second, the varying scales of license plates lead to difficulties in multi-scale detection. To address these issues, the authors propose a multi-oriented and scale-invariant license plate detection (MOSI-LPD) method based on CNNs, which outperforms existing approaches in detecting license plates with different orientations and multiple scales. In [4], a comparison of object detection methods for vehicle license plate recognition is performed using a new dataset for Turkish license plates labeled with \"car\" and \"license plate\" classes. The tested networks include a single-shot multibox detector (SSD), a faster region-based convolutional neural network (Faster R-CNN), and region-based fully convolutional networks (R-FCNs). The results show that SSD-based solutions can better detect larger objects (cars) but have difficulties detecting smaller license plates, while Faster R-CNN achieves the highest accuracy of 97.9%. The authors of [5] propose the edge-guided sparse attention network (EGSANet) for license plate detection, which utilizes edge contours and solves the problem of real-time detection. They also adjust parameters of the YOLOv3 network [6] to improve accuracy on a Malaysian license plate dataset from 87.75% to 99%. Research in [7] proposes a convolutional neural network called optimal k-means clustering-based segmentation and convolutional neural networks (OKM-CNNs) for license plate detection, character segmentation, and number recognition. The proposed model employs the improved Bernsen algorithm (IBA) and connected component analysis (CCA) for license plate detection and localization, and the simulation results demonstrate effective performance compared to other methods. In [8], an accurate and real-time automatic license plate recognition (ALPR) method called VSNet is proposed, which includes two CNNs: VertexNet for license plate detection and SCR-Net for license plate recognition. The proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement in the error rate, achieving recognition accuracy superior to 99% on the Chinese City Parking Dataset (CCPD) and the Application-Oriented License Plate (AOLP) dataset with an inference speed of 149 frames per second (FPS).