  With the large uses of the intelligent systems in different domains, and in
order to increase the drivers and pedestrians safety, the road and traffic sign
recognition system has been a challenging issue and an important task for many
years. But studies, done in this field of detection and recognition of traffic
signs in an image, which are interested in the Arab context, are still
insufficient. Detection of the road signs present in the scene is the one of
the main stages of the traffic sign detection and recognition. In this paper,
an efficient solution to enhance road signs detection, including Arabic
context, performance based on color segmentation, Randomized Hough Transform
and the combination of Zernike moments and Haralick features has been made.
Segmentation stage is useful to determine the Region of Interest (ROI) in the
image. The Randomized Hough Transform (RHT) is used to detect the circular and
octagonal shapes. This stage is improved by the extraction of the Haralick
features and Zernike moments. Furthermore, we use it as input of a classifier
based on SVM. Experimental results show that the proposed approach allows us to
perform the measurements precision.
