Accessibility & Assistive Technology
Personal Assistant for Deaf and Mute
Year:
2024
Timeline:
B.Tech Final Year Project
B.Tech Project - Innovative mobile application for enhanced accessibility and seamless communication.
This is my B.Tech final year project - an innovative mobile application designed to assist deaf and mute individuals in communication. The application was developed using the Flutter framework and integrates MediaPipe for real-time gesture recognition. I implemented advanced real-time gesture recognition algorithms that can interpret sign language gestures, and sophisticated text-to-speech conversion features that enable users to communicate effectively. The project combines mobile development, machine learning, computer vision, and natural language processing to create a comprehensive communication support system.
Challenges.
The major challenges included implementing real-time gesture recognition with high accuracy, integrating multiple AI/ML technologies seamlessly, and ensuring the application works reliably across different devices and lighting conditions.
Solutions.
I used MediaPipe for robust gesture recognition and integrated TensorFlow models for machine learning capabilities. Python was used for backend processing, while Flutter provided a cross-platform mobile solution. Computer vision algorithms were implemented to accurately detect and interpret sign language gestures in real-time, and natural language processing was used to convert text to speech effectively.
Tech Stack.
Flutter, MediaPipe, Python, TensorFlow, Machine Learning, Computer Vision, Natural Language Processing
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