Islam, Kh Tohidul and Wijewickrema, Sudanthi and Raj, Ram Gopal and O’Leary, Stephen (2019) Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks. Journal of Imaging, 5 (4). p. 44. ISSN 2313-433X, DOI https://doi.org/10.3390/jimaging5040044.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | street sign; autonomous vehicle navigation; computer vision; artificial neural networks |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Mar 2020 11:30 |
| Last Modified: | 22 Mar 2020 11:30 |
| URI: | http://eprints.um.edu.my/id/eprint/24090 |
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