The ASL community often struggles to relate to the rest of the world because of the fact that most normally-hearing people simply do not know sign language. Our project uses machine learning as an efficient and effective learning tool for those who do not otherwise know sign language.
What it does
The webcam takes in data and allows users to see if their signs match the letter they intend to display. When the sign is accurate, the correct letter pops up.
How we built it
We used a very simple Flask web application concurrently with HTML and CSS along with openCV to create our models.
Challenges we ran into
Integrating openCV with our web application made it quite difficult at first.
Accomplishments that we're proud of
Creating an efficiently trained machine learning model for sign language detection.
What we learned
How to use Flask's various web functionalities as a way to host and deploy machine learning models.
What's next for ASLIntelligence
We want to use the return values of the model to help facilitate the spelling of more complex words and sentences, somewhat like TypingClub or MonkeyType.