Next-Gen Smart Vehicle Number Plate Recognition with Dynamic Character Encoding for Lightning-Fast and Accurate Detection

Abstract

This research explores the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs with LSTM), for robust Automatic Number Plate Recognition (ANPR) in real-world scenarios. The system effectively detects and recognizes license plates from images captured in real-world environments. By treating the character string recognition as a sequence labeling problem, the system achieves a high success rate of 97%. This research demonstrates the significant potential of deep learning in enhancing the accuracy and reliability of ANPR systems, enabling applications in intelligent transportation systems, traffic management, and law enforcement.