Abstract

Automatic License Plate Recognition (ALPR) in Bangladesh faces challenges due to the complexity of Bangla script and low-resolution CCTV footage. This research introduces a YOLOv8-based deep learning model tailored for Bangladeshi license plates, enhancing plate localization, character segmentation, and Bangla script recognition using Easy-OCR. The model leverages Roboflow for data collection, annotation, and augmentation, training on a dataset of 2600 images captured under diverse conditions, including low resolution, harsh weather, and partial obstructions. The model distinguishes license plates from other rectangular objects on vehicles, achieving 94.8% detection and recognition accuracy. These results demonstrate the system's robustness in real-world scenarios, contributing to improved road safety, traffic management, and law enforcement in Bangladesh, marking a significant advancement in ALPR technology for the region. This research marks a significant advancement in ALPR technology for Bangladesh, contributing to improved road safety, efficient traffic management, and enhanced law enforcement capabilities.