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@article{158768,
        author = {Harsh Shirke and Komal Satam and Shreyas Shete and Om Rane and Sowmyashree},
        title = {Classification of Traffic Signs using Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {703-705},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158768},
        abstract = {Traffic signs are the important aspect of people's safety while driving. Though there are traffic signs at each corner of the road to indicate some instruction. We often find it difficult to understand what that traffic sign actually mean. There are in total 43 different traffic signs according to German Traffic Sign Recognition Benchmark. Our system approaches to solve this problem of classification and identification of these various traffic signs. Our system uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset to identify and classify traffic signs using Convolutional Neural Network. As the pre-processing needed in CNN is less, We prefer using Convolutional Neural Network. The system captures the image of the traffic sign in real time and classify the image from the 43 classes to identify the traffic sign.},
        keywords = {Convolutional Neural Network, German Traffic Sign Recognition Benchmark, Traffic Signs.},
        month = {},
        }