Inability of deaf and mute people to communicate with the hearing and speaking population creates a significant communication barrier. Current solutions such as sign language interpreters are expensive, not readily available, and often not practical for everyday use. Therefore, there is a need for an affordable and accessible solution that can enable effective communication between deaf and mute individuals and the rest of society.
A sign language translator that takes video as input and uses machine learning to recognize signs, converting them into text. This text is then synthesized into audio output using text-to-speech technology, allowing deaf and mute individuals to communicate with others. The solution can be deployed as a mobile application or web-based platform, making it easily accessible and affordable. It can also be enhanced with additional features such as translation into multiple languages and support for more sign languages.
- Video input: The system accepts video input of sign language gestures for processing.
- Machine learning: The system uses machine learning algorithms to recognize signs and convert them into text.
- Text-to-speech: The text output is synthesized into audio output using text-to-speech technology.
- Mobile app and web platform: The system can be accessed through a mobile application or web-based platform.
- Translation: The system can translate sign language into multiple languages to cater to a wider user base.
- Support for multiple sign languages: The system is designed to recognize and translate multiple sign languages.
- App FrontEnd- Swift,Flutter
- Web FrontEnd- React,Vite,Tailwind CSS
- Machine Learning - Python (Tensorflow, openCV,Teachable,Mediapipe
The implementation of the sign language translator involves the use of several technologies and tools. Firstly, the system uses computer vision and machine learning algorithms to recognize and interpret sign language gestures captured in video format. For this, we can use OpenCV, a popular computer vision library, and a trained deep learning model for sign language recognition. Once the gestures are recognized, the system converts them into text using natural language processing (NLP) techniques. The text is then synthesized into audio using text-to-speech (TTS) conversion tools such as the Google Text-to-Speech API. The final output is delivered through a web or mobile application interface, which takes in the video input and provides the audio output. The application can be developed using web development frameworks such as Flask, Django or React Native for mobile app development. The overall implementation involves integrating these various components into a seamless system that can accurately recognize and translate sign language into speech in real-time.
- Future enhancements for the sign language translator include improving sign language recognition accuracy and adding speech-to-sign language translation.
- Integration with web3 technology could include blockchain-based data privacy and security measures.
- Integration with DeFi platforms could enable deaf users to participate in financial transactions.
- Blockchain-based identity solutions could allow deaf users to create secure and immutable identities and authenticate themselves on the platform.
- Use of cloud-based servers to accommodate increasing demand for the service.
- Distributed computing to enable faster processing of user inputs.
- Leveraging machine learning algorithms to improve recognition capabilities and provide a more accurate and reliable translation service.
- Collaboration with sign language communities to ensure that the platform is accessible and useful to as many users as possible.
- Expanding the platform to include additional sign languages to cater to a wider audience.
- Providing a hardware-software interface that allows users to access the platform on-the-go.
- Integrating the platform with other assistive technologies to provide a more holistic and comprehensive solution for users.
This project is licensed under the MIT License - see the LICENSE file for details.