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@article{151296,
        author = {Arpit Seth and VijayKumar A },
        title = {A traffic sign classifier model using Sage maker},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {450-454},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151296},
        abstract = {Traffic sign recognition is vital in driver help systems that relieve the driver's work, still as intelligent autonomous vehicles. We have a tendency to purpose during this paper Traffic sign classification is a crucial task for self-driving cars. During this research, a Deep Network referred to as LeNet are going to be used for traffic sign pictures classification. The dataset contains forty three totally different categories of pictures. This structure is split into 2 parts: traffic sign identification and traffic sign classification. ADASs area unit being designed for a spread of functions, together with communications, road mark detection, road sign recognition, and pedestrian detection. This structure is split into 2 parts: traffic sign identification and traffic sign classification. The strategies for detection and recognizing traffic signals area unit mentioned during this article. For traffic sign detection and recognition, varied strategies like colour segmentation and also the RGB to HSI model area unit used. HOG operate, form which means, and different factors contribute to recognition.},
        keywords = {},
        month = {},
        }