Real-Time Traffic Sign Detection And Classification Using Machine Learning And Optical Character Recognition
Author ORCID Identifier
Hasan Ferdowsi:https://orcid.org/0000-0003-2304-3399
Publication Title
IEEE International Conference on Electro Information Technology
ISSN
21540357
E-ISSN
21540373
ISBN
9781728153179
Document Type
Conference Proceeding
Abstract
Autonomous vehicle development is currently progressing at a very fast pace and traffic sign detection and classification has an important role in it. This paper analyzes a few possible approaches of doing this task in real-time using a portable system. The final solution uses a convolutional neural network for detection and classification combined with a custom optical character recognition algorithm for speed limit signs. The training and testing dataset is based on a combination of the Belgian Dataset, German Dataset, as well as images taken while driving in Illinois, United States.
First Page
480
Last Page
486
Publication Date
7-1-2020
DOI
10.1109/EIT48999.2020.9208309
Keywords
autonomous vehicles, image processing, optical character recognition, traffic signs
Recommended Citation
Ciuntu, Victor and Ferdowsi, Hasan, "Real-Time Traffic Sign Detection And Classification Using Machine Learning And Optical Character Recognition" (2020). NIU Bibliography. 188.
https://huskiecommons.lib.niu.edu/niubib/188
Department
Department of Computer Science; Department of Electrical Engineering
