Robust Efficient License Plate and Character Detection System Based on Simplified CNN

Current license plate recognition systems struggle with image noise reduction and license plate feature detecting processes. This paper presents an efficient and highly accurate license plate detection and character detection program based on the YOLO neural network, which is a simplified CNN-based neural network frame for robust image processing systems. Different than most approaches, the system we proposed simply requires a prioritized analysis of the dataset in order to evaluate potential noises inside images so that program implementations could be more effective and more targeted to design and optimize with YOLO neural network. With our presented system, the accuracy of license plate detection improves from 63% which is performed by traditional image processing methods to 90.3%.