Abstract for Traffic Sign Recognition System using Convolutional Neural Networks (CNN) in Python:
This project introduces a Traffic Sign Recognition System implemented in Python, leveraging Convolutional Neural Networks (CNN). The primary objective is to develop a robust and accurate system for automated detection and classification of traffic signs from images or video streams. The proposed solution utilizes CNNs to automatically learn and extract hierarchical features from traffic sign images, enabling effective recognition. The system is trained on a labeled dataset containing various traffic sign categories, and it employs transfer learning to enhance its performance. The project aims to contribute to road safety by providing a reliable tool for real-time traffic sign detection and classification. The use of Python and CNNs ensures a practical and efficient solution, demonstrating the potential for deployment in advanced driver-assistance systems (ADAS) and autonomous vehicles.