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

Department

Department of Electrical Engineering

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