American Sign Language AI/ML Model

Inspiration

Imagine a world where conversations flow effortlessly, regardless of spoken language. This vision inspired me to develop an AI model that translates American Sign Language (ASL) into English, fostering seamless communication between deaf and hearing communities.

What it does

This ASL recognition model empowers real-time communication by translating signed gestures into written English. It bridges the gap between deaf and hearing communities, promoting inclusivity in everyday interactions.

How we built it

I leveraged the power of TensorFlow, a leading machine learning framework, alongside Python for development and OpenCV for computer vision tasks. This combination allowed us to train the model to recognize and interpret complex hand shapes and movements.

To showcase the project, I've also built a web application showcasing the details, how to install and how to use the application.

Challenges we ran into

Encountering challenges like wrong TensorFlow shape nodes is inevitable in any AI project. It pushed me to dive deeper into machine learning concepts and refine our approach, ultimately strengthening the model's accuracy through fine tuned pooling layers.

Accomplishments that we're proud of

Developing a functional sign language model that detects a foundational set of signs (5 letters and 2 phrases) with a remarkable 90% accuracy. This achievement demonstrates the potential of AI to bridge communication gaps and empower the Deaf and hearing communities to connect seamlessly. It showcases the model's effectiveness and lays a strong foundation for further development.

What we learned

This project opened my eyes to the complexities of sign language recognition with AI. Beyond hand gestures, factors like facial expressions and movement play a crucial role in accurate translation. The importance of a robust or a sort of like variety dataset also became clear. High-quality data that has diverse hand signals and backgrounds is essential for ensuring the model's success.

What's next for American Sign Language AI/ML Model

This project is just the beginning! I'm excited to explore the potential of this technology further. Here's a glimpse into what's next: I'm reaching out to the National Association of the Deaf (NAD) and other sign language organizations (like ASL) to share our findings and explore how our model can be integrated into existing resources. I'm committed to ongoing research and development. This includes fine-tuning the model by adjusting the network architecture. This could be anywhere from experimenting with different network structures, such as adding or removing layers in the Deep Neural Network (DNN). Optimizing activiation functions is also relevant. We'll explore different activiation functions within the model to improve its accuracy. I'll also utilize continuous learning. I'll keep feeding the model more data to enhance its sign language recognition capabilities.

Built With

• ejs
• express.js
• node.js
• pandas
• python
• tensorflow