A Convolution Neural Network-Based System for Licensed Number Plate Recognition

India's high population density and a large number of vehicles on the roads pose significant challenges in effectively managing traffic. Car license plate detection technology can assist in traffic management by identifying and monitoring vehicles that violate traffic rules, such as speeding, driving on the wrong side of the road and disobeying traffic signals. In real scenarios, the identification of licensed number plate detection is a challenging task because manual identification can be a resource-intensive and inefficient process that is not scalable beyond a certain limit. Therefore, manual identification is not a practical solution for large-scale license plate identification needs. This work creates a system for dynamically identifying license plates using convolutional neural networks (CNNs). The approach involves segmenting the detected plates and performing character recognition. The system employed a CNN trained on synthetic images and fine-tuned with real license plate images to detect and recognize license plates. The performance of the proposed CNN model is further evaluated which results in an average processing time of 4.88 seconds per image and an accuracy of 94%. Additionally, the model has performed better as compared to existing techniques which can be implemented in real scenarios for better parking management, toll collection, crime detection and traffic management.