SignSense

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  General image how the model detects human palm gestures

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  C

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  Thank you

Inspiration

Creating a sign language detection system can have a significant social impact, breaking down barriers and empowering individuals to express themselves more effectively in various settings.

What it does

By interpreting sign language gestures from images or videos, the system enhances communication, fosters inclusivity in education and public services, empowers the deaf community, and bridges language diversity.

How we built it

Using opencv, model selection, evaluation metrics, real-time implementation, user feedback, and ethical considerations. 

Challenges we ran into

Multipe errors occured while using opencv for data collection due not having high quality devices for image capturing.

Accomplishments that we're proud of

Even though the hardware configurations were not that good we tried our best to implement the project in available resources.

What we learned

Implementing classification models in machine learning as well as got an hands on experience with opencv.

What's next for SignSense

Collecting more data with due consent of people and training extensively the model for achieving a 100% accuracy.

Built With

• keras
• mediapipe
• numpy
• opencv
• os
• pickle
• python
• vscode