Inspiration
To not let deaf people feel Isolated and moreover to not let them feel different among people.
What it does
Sign Language Detection and Word Translation. Sentence Formation from Detected Words Speech Output for Detected Signs
How we built it
Challenges we ran into
It was our first time hackathon & we all were new to ML. At first, our accuracy was just 0.2 then we reached to 0.4, 0.8 and finally we could make it to 100%. Dealt with Kernel Crash. camera not accessible. Mac and opencv compatibility issue.
Accomplishments that we're proud of
100% accuracy words to speech Implementation First time implementation of ML model and being able to complete the project with fulfilling the higher expectation features.
What we learned
ML models Experience of being a first time hackathon 2 sleepless night learnt American sign language
What's next for Sign-Connect
Implement it into hardware (at public places, easy-to-carry hardware)
Built With
- flow
- lstm
- machine-learning
- mediapipe
- opencv
- python
- tensor
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