Sign- bridge ai

SignBridge is an advanced web application designed to bridge communication between hearing and non-hearing individuals. It uses modern machine learning to provide real-time sign language recognition and translation. With SignBridge, users can convert sign language gestures into text and typed text into animated American Sign Language (ASL) fingerspelling. This tool is built with a Python Flask backend and a Next.js/React frontend, ensuring a smooth and responsive user experience. The application includes a curated set of SVG assets for ASL fingerspelling, making it a comprehensive solution for sign language translation.

Benefits

SignBridge offers several key advantages:

• Real-time Translation:Converts sign language gestures into text instantly using a webcam and advanced machine learning models.
• Text-to-Sign Translation:Converts typed text into animated ASL fingerspelling, making communication easier for both hearing and non-hearing individuals.
• High Accuracy:The machine learning backend achieves up to 99% accuracy on benchmark datasets, ensuring reliable translations.
• Modern UI:The interface is responsive, accessible, and visually appealing, built with Next.js, React, and Tailwind CSS.
• Extensible:The modular codebase allows for easy addition of new features or support for additional sign languages.

Use Cases

SignBridge can be used in various settings to facilitate communication between hearing and non-hearing individuals:

• Educational Institutions:Helps teachers and students communicate effectively in classrooms.
• Healthcare Settings:Assists healthcare professionals in communicating with patients who use sign language.
• Workplaces:Enhances communication in professional environments where sign language is used.
• Everyday Communication:Provides a convenient tool for personal use, allowing friends and family to communicate more easily.

Additional Information

SignBridge is an open-source project available under the MIT License. It is built using a combination of TypeScript, Python, CSS, and JavaScript. The project includes a Flask API for sign recognition and a Next.js/React frontend for the user interface. The machine learning model used for sign recognition is YOLOv8, known for its high accuracy and efficiency. The application also includes a set of SVG assets for ASL fingerspelling, which are used to animate the text-to-sign translation.

The project is designed to be extensible, allowing developers to add new features or support for additional sign languages. The codebase is modular, making it easy to maintain and update. SignBridge is a valuable tool for anyone looking to improve communication between hearing and non-hearing individuals.