Real-Time Traffic Sign Detection And Classification Using Machine Learning And Optical Character Recognition
Author ORCID Identifier
IEEE International Conference on Electro Information Technology
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.
autonomous vehicles, image processing, optical character recognition, traffic signs
Ciuntu, Victor and Ferdowsi, Hasan, "Real-Time Traffic Sign Detection And Classification Using Machine Learning And Optical Character Recognition" (2020). NIU Bibliography. 188.
Department of Computer Science; Department of Electrical Engineering