Published Paper Details:

Maintaining safe driving practices is of utmost significance in the modern transportation system. Inadequately placed traffic signs are a leading cause of accidents that put drivers and passengers in danger. The purpose of traffic signs is to alert drivers, pedestrians, and other road users of critical information. Visibility, positioning, and motorist understanding are all crucial to the success of traffic signs. Innovations in road safety solutions have been developed in recent years thanks to technological advances. To combat this serious problem, we introduce "Traffic Sign Viewer," a novel approach to enhancing the readability of traffic signs and, in turn, raising everyone's level of awareness and road safety. The overarching goal of this effort is to provide a method or strategy that aids in enhancing road safety by raising drivers' and pedestrians' awareness of and sensitivity to traffic indicators. The goals include better traffic sign visibility and awareness, safer drivers and passengers, better use of cutting-edge technology, and fewer traffic accidents and fines. The proposed study will use machine learning algorithms, the computer vision method, and deep learning algorithms to accomplish its goals. OpenCV's implementation is deliberate and purposeful so that its intended benefits may be extracted. A supervised learning model is then used to construct a CNN, which is then used to identify traffic signs. The work here sheds light on detecting traffic signs and disseminating such data to drivers and passengers. Improvements in the following areas of the road environment are made possible by the planned work: Increased inter-vehicle communication, familiarity with the road environment, and the significance of traffic signs all contribute to safer driving conditions.