This study focuses on the crucial problem of motorcycle riders circumventing helmet restrictions by introducing an Automatic Number Plate Recognition (ANPR) system specifically designed to detect and penalize riders who do not comply with the regulations. The system employs sophisticated computer vision algorithms to autonomously identify motorcyclists who are not wearing helmets by analyzing photos or video streams in real-time. The proposed ANPR system utilizes advanced image processing algorithms and deep learning approaches to improve law enforcement skills in enforcing road safety and ensuring compliance with helmet laws. The process entails creating a strong Automatic Number Plate Recognition (ANPR) model that is trained on a broad dataset of motorbike riders in different settings. The technology not only identifies license plates but also utilizes advanced helmet detection techniques to identify motorcyclists who do not comply with regulations. The suggested approach aims to enhance public safety efforts by automating the enforcement of helmet rules, thereby decreasing the occurrence of head injuries among motorcycle riders. By employing this ground-breaking method, law enforcement organizations may effectively oversee and uphold adherence to helmet regulations, cultivating a more secure road atmosphere for both motorcyclists and the general populace.