Automatically identifying traffic signs is a challenging and time-consuming process. As the academic community pays more attention to traditional algorithms for vision-based detection, tracking, and classification, three main criteria drive the investigation, they are detection, tracking, and classification. It is capable of performing detection and identification operations to minimize traffic accidents and move towards autonomous cars. A novel method proposed in this paper is based on moment invariants and neural networks for performing detection and recognition with classification, and it also includes automatic detection and identification of traffic signs and traffic board text that uses colour segmentation. Aside from the proposed structure, it is also required to identify the potential graphic road marking with text. This research article contains two algorithms, which are used to accurately classify the board text. The detection through image segmentation and recognition can be done by using the CNN algorithm. Finally, the classification is performed by the SVM framework. Therefore, the proposed framework will be very accurate and reliable with high efficiency, which has been proven in many big dataset applications. The proposed algorithm is tested with various datasets and provided good identification rate compared to the traditional algorithm.
Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
