Recognition of Speed Limit from Traffic Signs Using Naive Bayes Classifier

The detection and recognition of road speed limit signs is an important task in advanced driver assistance system (ADAS). The speed limit signs are important in informing the driver about allowable speed in a particular area. It increases the safety, as it provides information about the circumstances of the road. Different systems are being implemented by the government authorities to prevent accidents due to over speed. Here the proposed system provides efficient detection and identification of speed limit signs. The system operates in the following way: first it involves the detection of the sign board and then performs segmentation followed by geometric detection. Hough transform algorithm is employed to detect the sign board with saliency based approach. Second the system detects the characters from the extracted sign board. Finally it involves recognition of speed limit sign using Naive Baye’s employed classifier. The algorithm is successfully tested and shows 94% accuracy.