Publication

Abstract : In the world of artificial intelligence and advancement in technologies, big companies like Tesla, Uber, Google, etc. are operating on independent vehicles and self-riding cars. The content or image identification and acknowledgment from traffic boards is a difficult issue. The quantity of significant application zones is reliant upon text discovery and acknowledgment, including progressed driver help frameworks, street looking over, and self-sufficient vehicles. In this proposed work, a framework for programmed discovery and acknowledgment of text and image in rush hour traffic signs is proposed. The present work uses a German traffic sign recognition dataset that contains diverse traffic signs composed of arbitrary roadsides. This dataset contains more than 50,000 images of 43 different classes of traffic signs. The work can be divided into two stages; the first stage is the observation of the area and the second is character identification. For textual content observation in signboard and recollection reason, the optical individual identification technique is used. The experimental results show that the proposed system achieved a better accuracy for text detection and text extraction.