Sign Recognition

Traffic Sign Recognition is a computer vision project that focuses on automatically detecting and classifying traffic signs from images using deep learning techniques. The goal of the system is to help machines understand road signs, which is an essential component in intelligent transportation systems and autonomous driving technologies.

The project uses a convolutional neural network (CNN) model trained on a traffic sign dataset containing multiple classes of road signs. The model learns visual patterns from thousands of labeled images and can accurately classify unseen traffic sign images during testing.

The system workflow begins by preprocessing the input images, including resizing, normalization, and data augmentation to improve model generalization. The processed images are then passed to the CNN model, which extracts important visual features and predicts the corresponding traffic sign class.

After training, the model is evaluated using a separate test dataset to measure performance and classification accuracy. The trained model can then be used to recognize traffic signs in new images and can be integrated into real-time applications such as driver assistance systems or autonomous vehicles.

Technologies used in this project include Python, deep learning frameworks, convolutional neural networks (CNN), and computer vision libraries for image processing and model training.

This project demonstrates how deep learning can be applied to real-world transportation problems and serves as a foundation for building more advanced autonomous driving systems.