Paper Info
Title
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.
Year
DOI
Venue
2022
10.1142/S0218001422500227
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Decision tree, deep learning, license plate detection, license plate recognition, motorcycle license plates
Journal
36
Issue
ISSN
Citations 
05
0218-0014
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chun-Ming Tsai100.34
Frank Y. Shih2110389.56