An Efficient Detection and Recognition System for Multiple Motorcycle License Plates Based on Decision Tree

Abstract

The automatic detection and recognition for motorcycle license plates present a very challenging task since they appear more compact and versatile than vehicle license plates. In this paper, we present an efficient detection and recognition system for motorcycle license plates based on decision tree and deep learning. It can be successfully carried out under various conditions, such as frontal, horizontally or vertically skewed, blurry, poor illumination, large viewing distances or angles, distortions, multiple license plates in an image, at night or interfered with brake lights, and headlights. Experimental results show that our system performs the best when testing with multiple license plates images under different conditions as compared against six state-of-the-art methods. Furthermore, our detection and recognition system have shown more accurate results than three commercial automatic license plate recognition systems in evaluation using accuracy, precision, recall, and F1 rates.