@article {63267,
	title = {Speed limit sign detection and recognition system using SVM and MNIST datasets},
	journal = {Neural Computing and ApplicationsNeural Computing and Applications},
	volume = {31},
	year = {2019},
	month = {2019},
	pages = {5005{\textendash}5015},
	abstract = {This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81\% with a precision of 99.08\% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39\% and 98.82\% with a precision of 99.05\% and 98.78\%, respectively, within a processing time of 11.22\&nbsp;ms.},
	author = {Saadna, Yassmina and Behloul, Ali and Mezzoudj, Saliha}
}
