• Akshata V. S
• Subarna Panda

The traffic signs engraved on the roads these days enhance traffic safety by informing the driver of speed limits or any further possible dangers such as deep curvy roads, imminent repair road works or any pedestrian crossings. This paper covers three-stage the real-time Traffic Sign Recognition and classification system, which consists of image segmentation, detecting the traffic sign and a classification phase based on the input image. To extract red regions in the image the colour enhancement technique is used. The detection, classification and recognition are performed using Convolutional Neural Networks (CNN) to identify the content of the traffic signs found.

TRAFFIC SIGN RECOGNITION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

"TRAFFIC SIGN RECOGNITION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.186-204, February-2019, Available :http://www.jetir.org/papers/JETIRAB06034.pdf

"TRAFFIC SIGN RECOGNITION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp186-204, February-2019, Available at : http://www.jetir.org/papers/JETIRAB06034.pdf