Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks
Document Type
Article
Publication Date
1-1-2019
Abstract
Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera. © 2019 by the authors.
Keywords
street sign, autonomous vehicle navigation, computer vision, artificial neural networks
Divisions
fsktm
Publication Title
Journal of Imaging
Volume
5
Issue
4
Publisher
MDPI