Conceptual Framework for Cost-Effective Automatic Number Plate Recognition System

ANPR is a must for traffic control, law enforcement and automated toll collection systems. Traditional ANPR solutions are expensive, rely heavily on hardware and not suitable for mass adoption. A low-cost, high-performance ANPR system on a Raspberry Pi 4 device with camera module and Optical Character Recognition (OCR) capabilities. The system uses a Convolutional Neural Network (CNN) for OCR which can identify license plates with high accuracy even under different lighting conditions and resolutions. The pre-processing pipeline of the image includes noise removal and gray scaling to make extracting edges simpler, which aims for better license plate visibility in crowded frames. These pre-processed images are further passed to a plate localization extraction trained CNN so as the make it invariant of distortion and locale ensuring better accuracy in such number plate detection applications. This low-cost answer represents a viable option to traditional ANPR systems for larger scale applications in traffic control, law enforcement and automatic toll processing. In future iterations, we look to increase performance and robustness by providing more data from which the system can learn. Moreover, the incorporation of advanced functionalities like cloud-based analytics would strengthen system features that will aid in smart city infrastructure integrations. The advancements of these developments aim to increase ANPR system throughput at a scale that will play role in enabling safer and more efficient transportation systems.