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Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks

1
Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia
2
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
J. Imaging 2019, 5(4), 44; https://doi.org/10.3390/jimaging5040044
Received: 27 December 2018 / Revised: 18 March 2019 / Accepted: 18 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue Trends in Machine Learning for Visual Computing)
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. View Full-Text
Keywords: street sign; autonomous vehicle navigation; computer vision; artificial neural networks street sign; autonomous vehicle navigation; computer vision; artificial neural networks
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Islam, K.T.; Wijewickrema, S.; Raj, R.G.; O’Leary, S. Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks. J. Imaging 2019, 5, 44.

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