Sign Language to Speech Conversion System – Project Brief Objective: To bridge the communication gap between the deaf/mute community and the hearing population by converting sign language gestures into audible speech in real time.
Problem Statement: People with speech and hearing impairments often struggle to communicate with others who do not understand sign language. There is a need for a real-time, portable, and affordable system that can interpret sign language and convert it into spoken words.
Proposed Solution: A wearable or mobile-based AI system that uses computer vision and machine learning techniques to:
Capture sign language gestures (via camera, e.g., sunglasses-mounted or smartphone).
Interpret the gestures using deep learning models (e.g., CNNs, RNNs).
Convert recognized signs into speech output using a text-to-speech engine.
Key Features: 1)Real-time gesture recognition 2)Multilingual speech output 3)Lightweight and user-friendly design 4)Works offline or with limited connectivity 5)Customizable vocabulary 6)Technology Stack: Hardware: Camera (e.g., on smart glasses), Microcontroller (optional), Speaker Software: Computer Vision: OpenCV, MediaPipe
AI/ML: TensorFlow / PyTorch, CNN for image classification
TTS: pyttsx3 / gTTS
Platform: Python, Android (for mobile deployment)
Applications: 1)Communication aid for the hearing impaired 2)Inclusive education tools 3)Customer service desks 4)Public service interactions (hospitals, banks, etc.)
Future Enhancements: Integration of facial expressions and body posture for more context
Bidirectional communication (speech to sign language)
Cloud-based real-time translation and voice modulation