Inspiration
The inspiration for this project came from the desire to bridge the communication gap between American Sign Language (ASL) users and machines.
What it does
This project aligns with accessibility goals, aiming to empower ASL users to interact with digital systems more effectively.
How we built it
With advancements in computer vision and deep learning, we saw an opportunity to develop a real-time sign language recognition system that could interpret ASL gestures with high accuracy and enable seamless human-computer interaction.
Challenges we ran into
ASL Grammar Complexity: Accounting for the unique grammar and sentence structure of ASL, which differs from spoken/written English.
- Data Imbalance: Collecting enough training data for each gesture while avoiding over-representing any single action.
- Real-time Performance: Reducing latency to ensure predictions were smooth and accurate in live scenarios.
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