As technology continues to advance, the demand for fast and accurate license plate readers be-comes increasingly critical. Automating the process of license plate reading and recognition offers significant benefits to law enforcement, traffic management, and vehicle monitoring systems. Traditional methods of license plate recognition often struggle with performance issues under varied lighting conditions and when dealing with diverse plate designs. Modern technological advancements, particularly in the field of artificial intelligence, have begun to address these challenges. Convolutional Neural Networks (CNNs) have proven particularly effective in image recognition tasks, paving the way for improved Automated License Plate Recognition (ALPR) systems. This research proposes a CNN-based solution specifically for Indian automobile registration number plates, addressing limitations of traditional methods. The proposed method involves training a CNN model on a large dataset of Indian license plate images to accurately detect and recognize plates even under challenging conditions. Extensive testing demonstrates superior performance in terms of accuracy, precision, and recall compared to existing methods. This research not only contributes to the advancement of ALPR technology but also provides a scalable and efficient solution suitable for real-world applications, enhancing vehicle identification and monitoring processes in traffic management and law enforcement.

Accuracy, Convolutional Neural Networks, Deep Learning, License Plate Detection, Precision, Recognition, Traffic Management, Vehicle Registration, Visual Recognition, Real-time Processing.