Technological advances within the automotive industry in past few years has turned the attention of both industries and academic research teams to design advanced driving assistance systems for traffic management and collision avoidance. An effective way to achieve the aforementioned goal is the detection of traffic signs. Design of a new intelligent driver assistance system based on traffic sign detection with Persian context is concerned in this paper. The primary aim of this system is to increase the precision of drivers in choosing their path with regard to traffic signs. To this end, a new framework that implements fuzzy logic was used to detect traffic signs in videos captured along a highway from a vehicle. Implementing fuzzy logic in smart systems increases its inference and intelligent capabilities which result in better decision making in real-time conditions. In order to detect road sign’s texts, the combination of Canny Edge Detector Algorithms and Maximally Stable Extremal Regions (MSER) is used. Regions of an image that vary in properties, such as color or brightness, with respect to surrounding regions, are detected with the help of MSER algorithm. By using a multi-stage algorithm, Canny edge detector detects a wide range of edges in the acquired images. In order to join the individual characters for the final stage of detection of texts in traffic signs, a morphological mask operator is used. Finally, the recognition of the detected texts is carried out by employing MATLAB Optical Character Recognition (OCR). The overall accuracy of this new framework in detecting and recognizing texts is 90.6%.