Advancement in the field of computer vision in the recent past and deep learning technology have intensified the ideas of the prevalence of most value for assessing the state of development and maturity of advanced driver assistance systems (ADAS). ADAS technologies envisage to bring down traffic accident rates and make driving safer. Most of the automobiles being built in today’s world come with ADAS systems. Built in. The proposed work implements a mobile, image-optical character recognition for developing and using a cost-effective experimental-based ADAS system for real-time traffic sign detection on the road sign boards with destination data. The proposed work is an extra ADAS or even be useful to help the driver get through roads better. Hence, only a small percentage of studies, 19.3%, aimed towards reducing and mitigating air pollution out of all the studies done on urban areas. The plan involves the use of a low-resolution camera mounted in the frontal area of the vehicle that can be mechanically interfaced with a Jetson Nano or similar board. With the OCR system in line with the road sign boards, they are easily interpretable with speed. And accuracy and then fed to the driver’s dashboard. Allowing him to view important data he could have missed while driving.