Intelligent Automatic Number Plate Recognition System
Automatic Number Plate Recognition (ANPR) systems were created in order to address the challenges created due to rise in the volume, velocity and density of automobile vehicles. Most modern ANPR systems heavily depend on image processing techniques like contouring and gray-scaling to segment the license plate and use optical character recognition to extract the number from the manipulated image of the number plate. The major advantage of such systems is that they require less computational power and are quite cost-effective. But these systems process the complete images rather than just the region of interest. The more modern ANPR systems rely on object detection algorithms to overcome this challenge and process only the area of interest from the image. But these systems are not suitable for real-time applications due to high computational overhead and high processing time. To overcome the limitations of the existing ANPR systems, we propose an intelligent ANPR is a system for detecting and extracting number plate details from images or sequences of images of vehicles. The proposed deep learning model is based on an improved version of the You Only Look Once (YOLOv4) algorithm. The performance evaluation demonstrates that our model is able to recognize the number plates with an accuracy of above 95% under varied conditions such as high-speed moving vehicles, varying lighting, and vehicle dense area on Indian roads. The system is robust enough to detect the number plates of fast moving vehicles (speed≤ 80km/hr) as well as vehicles in highly traffic dense areas.
Email Address of Submitting Authorcherukuri@acm.org
Submitting Author's InstitutionVellore Institute of Technology: VIT University
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