Auranuch LorsakulMahidol University. Faculty of Engineering. Center for Biomedical and Robotics Technology (BART LAB)2011-03-182011-12-092018-01-242011-03-182011-12-092018-01-242011-03-182007https://repository.li.mahidol.ac.th/handle/20.500.14594/3369Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for driver assistance systems. This paper presents a study to recognize traffic sign patterns using Neural Network technique. Images are pre-processed with several image processing techniques, such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse. Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show highly accurate classifications of traffic sign patterns with complex background images as well as the results accomplish in reducing the computational cost of this proposed method.engMahidol UniversityTraffic sign recognitionIntelligence vehicleNeural networkArticleCenter for Biomedical and Robotics Technology (BART LAB), Department of Biomedical Engineering, Faculty of Engineering, Mahidol University