Deep learning development for automatic license plate detection with text extraction capability - Murshed Dokhan, Sabri Nasser Hussein; Nureize Arbaiy, Nureize Arbaiy (2024)
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