Vehicle Number Plate Recognition Using Adaptive Adaptive Recurrent Neural Network

Vehicle number plate recognition (VNPR) is a critical component in various transportation and security applications. This paper presents a novel approach to VNPR leveraging Adaptive Recurrent Neural Networks (ARNNs). Our method employs an ARNN architecture designed to process sequential data, allowing for efficient extraction of features from license plate images. We utilize a dataset consisting of labeled vehicle images for training and evaluation. Through extensive experimentation, we demonstrate the effectiveness of our approach, achieving high accuracy in license plate recognition tasks. Our results highlight the potential of ARNN-based methods in the field of VNPR, paving the way for improved automation and efficiency in transportation systems and law enforcement.