Welcome to SignSense! 🎉 Our mission is to bridge the communication gap between hearing-impaired individuals and the wider community by creating an accessible and fun sign language recognition system. Using everyday devices like smartphones and webcams, SignSense translates sign language into English alphabets, making communication easier and more inclusive. 🤝✨
- Create Benchmark Datasets 📸: Develop and curate datasets using smartphone cameras to enhance the accuracy and effectiveness of sign language recognition.
- Develop an Application 🌐: Build a user-friendly app that performs real-time sign language recognition and translation.
- Achieve High Accuracy 🏆: Exceed the accuracy of current sign language recognition solutions and provide reliable results.
Communication is a fundamental part of life, but for some, hearing or speech impairments can make it challenging. Sign language is a fantastic tool for the deaf and hard-of-hearing community, but there are two major hurdles: the scarcity of interpreters and the limited access to those who are available. We aim to overcome these challenges with an innovative solution! 🚀
Sign language is a powerful tool for bridging communication gaps, yet many struggle to understand it. Current systems often rely on high-end cameras, which can be a barrier. SignSense proposes using standard cameras from smartphones and webcams, combined with advanced machine learning techniques, to create a more accessible and accurate sign language recognition system. Our web portal will make it easier for everyone to communicate and support future research in this field. 🌍🔬
Sign language communicates through hand gestures, facial expressions, and body movements. It’s essential for those with hearing or speech impairments. The goal of sign language recognition (SLR) is to translate these gestures into readable text or speech. With cutting-edge technology and vision-based gesture recognition, SignSense is set to make human-computer interaction smoother and more natural. 🤖💬
- TensorFlow: For building and training our deep learning models. 🧠
- OpenCV: To handle image and video processing tasks. 📷
- NumPy: For numerical operations and handling data arrays. 📊
- MediaPipe Holistic: For hand tracking and pose estimation. ✋
- LSTM (Long Short-Term Memory): For predicting sequences and recognizing gestures over time. ⏳
See SignSense in action! Watch our demo video to get a sneak peek of how it works:
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Explore the architecture of SignSense with our detailed diagram that shows how the system components interact:
Check out the activity diagram to understand the workflow and processes involved in SignSense:
We love collaboration! Here’s how you can contribute to SignSense:
- Fork the repository on GitHub.
- Create a new branch (
git checkout -b feature-branch). - Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature-branch). - Create a pull request to share your updates with us.
Have questions or need support? Reach out to us:
- Email: kaustubhr7.work@gmail.com 📬
- GitHub: github.com/kaustubhr7/SignSense-Sign_Language_Interpreter 🖥️
Thank you for checking out SignSense! We’re excited to help make communication more inclusive and accessible for everyone. 🚀🌟