VEHICLE NUMBER PLATE DETECTION AND RECOGNITION USING DEEP LEARNING

Automatic recognition of vehicle license plates plays a vital role in modern Intelligent Transport Systems (ITS),offering an efficient method for identifying vehicles based on plate information. This work introduces a deeplearning-based approach employing Convolutional Neural Networks (CNN) to detect license plates and retrieveassociated vehicle owner data from a centralized database. The system includes two main components: anadministrative portal that enables traffic authorities to monitor violations and issue fines, and a user portal forindividuals to view their challan (fine) details. By utilizing the powerful pattern recognition capabilities of CNNs,the model achieves reliable performance across a range of environmental and lighting conditions. The system hasbeen trained with real-world image data, ensuring its effectiveness even when faced with challenges such as noise,blurriness, or low image quality. This solution reduces the need for manual intervention, streamlines traffic lawenforcement, and supports improved traffic oversight.