Deep learning development for automatic license plate detection with text extraction capability

Vehicle identification and tracking are essential in traffic surveillance systems, which prioritize safety and efficiency. Security personnel still manually observe medium-sized structures. Security officers physically record license plate numbers after visually examining passing automobiles. This process is time-consuming and error prone. This project aims to create a web-based deep learning system for automated license plate identification with text extraction at UTHM, Malaysia. A variety of car license plates will train the system. A convolutional neural network will immediately detect and interpret license plate text. The user may submit a vehicle photo to the online interface, and the system will recognize the license plate number and return a picture with the highlighted plate. The system will be tested on a set of concepts using accuracy and precision criteria. Traffic management and vehicle identification might employ the created technology