A Hybrid Artificial Intelligence-Based Approach for Accurate Number Plate Preprocessing and Recognition

Number plate recognition is an important component of smart transportation systems as it plays a significant role in automated traffic management, law enforcement, and road safety. In order to achieve accurate and effective recognition under various types of settings, this paper provides a thorough analysis of number plate preprocessing and recognition algorithms. Our system leverages computer vision and object detection techniques to accurately identify vehicles and extract their license plates. To ensure precise number plate recognition, we employ methods such as skew correction, noise reduction, grayscale conversion, and optical character recognition. Common issues with number plate recognition like slanted orientations and varying lighting conditions are addressed. Through the integration of these techniques, our system achieves a recognition accuracy of 98.1%, outperforming several existing approaches. Our approach attempts to offer insights into enhancing the accuracy and robustness of number plate recognition systems by assessing current approaches and their effectiveness.