Inspiration
While watching a movie, I noticed people using hand gestures to communicate with individuals who had difficulty speaking. This sparked an idea: What if technology could make such communication easier and accessible to everyone? That inspired me to build Sign Language Translation.
What it does
The project uses a live camera feed to recognize American Sign Language (ASL) gestures and translate them into English text, helping bridge communication gaps between hearing and non-verbal individuals.
How we built it
We built the project using Python and machine learning. OpenCV captures live video, while a trained LSTM model processes hand gestures and predicts their corresponding English meanings.
Challenges we ran into
There are a various number of challenges such as limited amount of dataset , also we had to make our own custom dataset , processor to run the model which took almost 2 hrs to run and sufficient RAM consumption. Every library is not compatible with other , so needed to find the ones which work with other without any problem.
Accomplishments that we're proud of
We are Proud to make such a interface which does not divide people who can speak and can't. It united people and does not make non-verbal different from others.
What we learned
The most fun part on making this project was to learn the ASL , every language has it's own language and also how interesting it is to communicate with others
What's next for Sign Translation
Making a hardware device for this and making it universal for translation , object detection and communicating effectively
Built With
- jupyter-notebook
- keras
- lstm
- python
- tensorflow
