# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
## 2 Related Work  
There have been several compelling projects-both in academia and industry-involving vehicle number plate recognition in different domains [1; 2]. This section focuses on existing work on the ANPR technology using machine learning [5; 6; 9; 10; 11; 12] and computer vision approaches [3; 4; 7; 8; 17]. [PERSON] et al. [11] proposed an ANPR method based on ML to address the challenge of the lack of uniform format of capturing and displaying number plates across different countries. The authors used a combination of three ML models, namely CNN, RNN, and BLSTM, to recognize number plates with diverse background and foreground color. The proposed method has achieved a slightly better classification rate compared to some existing ANPR approaches. Another application of deep learning in license plate recognition was proposed in [10]. The authors tried to address three technical challenges: (1) develop an algorithm to improve the quality of input images by handling the license plate skew, image noise, and license plate blur; (2) use different deep learning models (e.g., RCNN [18] and CNN [9]) in the license plate detection process to enhance the classification rate; and (3) compare the developed method to some public license plate datasets which contain different image resolutions and environmental complex conditions. [PERSON] et al. [16] implemented an ANPR system based on the YOLO detector for identifying the location of the license plate. The authors used a set of postprocessing rules for improving the recognition results, such as employing a different confidence threshold for different geographical regions. After locating the plate on the car's body, all plate characters are recognized simultaneously, avoiding the character segmentation process. The system is trained using plate images from several datasets, with the addition of various data augmentation techniques. The proposed method achieved an average classification accuracy of 96.8% over the used datasets. Similar to the work presented in [16], [PERSON] et al. [17] proposed an ANPR method for international license plate recognition based on the YOLO detector. The proposed layout detection algorithm was able to extract the bounding box of the characters in the plate in the correct sequence. The proposed system was evaluated using datasets from five countries, including South Korea, Taiwan, Greece, USA, and Croatia. The experimental evaluation showed that this system could recognize the actual number plate within 42 ms per image. An online ANPR system for recognizing dirty vehicle plate numbers for high-speed applications is proposed in [7]. The authors used the Connected Component Analysis (CCA) algorithm for character recognition in the plate. A dataset consists of 10,000 images captured by traffic cameras is collected for Persian license plates for evaluating the system. The system achieved classification accuracies of 98.7%, 99.2%, and 97.6% for plate detection, character segmentation, and plate recognition, respectively. Another technique is proposed in [8] to provide an affordable ANPR technology that can be adopted at a large scale in developing countries. The authors implemented a low-cost crowd-sourced system that can select the appropriate cameras for capturing the plate photos and improve the system's performance under various optoelectronic and environmental conditions. The developed optoelectronic model showed usefulness in selecting the surveillance cameras in streets, capturing the highest possible quality of the plates' photos. [PERSON] et al. [4] proposed an ANPR framework for detecting, segmenting, and recognizing various-shaped license plates. The proposed framework has a preprocessing module that converts the input image into a grey-scale version and applies arithmetic-based dilation. Then, the preprocessed image's vertical and horizontal edge densities are extracted and smoothed using a Gaussian filter.