Building a convolutional neural network to classify images of letters from American Sign Language.
American Sign Language (ASL) is a language used by the deaf and hard-of-hearing communities, allowing individuals to communicate using gestures, hand signs, and facial expressions. In this machine learning project, we aim to bridge the communication gap by building a Convolutional Neural Network (CNN) to classify images of ASL letters. This project is an exciting introduction to computer vision and deep learning, and it demonstrates the power of neural networks in recognizing intricate patterns.
This project showcases the potential of deep learning in addressing real-world communication challenges. By creating an AI model that can understand and classify ASL letters, we contribute to breaking down barriers between hearing and non-hearing communities. This project serves as an educational stepping stone into the world of machine learning and computer vision, empowering you to make a positive impact through technology.
To get started with this project, follow the steps outlined in the code and instructions provided in the Jupyter Notebook. The dataset and code are included in this repository, making it easy for you to explore, learn, and build upon this project.
This project can be extended in various ways, such as:
- Increasing the vocabulary by incorporating more ASL signs.
- Developing a real-time ASL translator using computer vision techniques.
- Expanding the dataset to include variations in hand gestures and lighting conditions.
This project was inspired by the need to make ASL more accessible and inclusive. I thank the creators of the ASL dataset and the supportive machine learning community for providing the tools and resources to make this project possible.