Sign-Speak Communicator: Real-Time AI Interpreter

The Sign-Speak Communicator is an AI-driven hardware and software project designed to bridge the communication gap for individuals with speech and hearing disabilities.

Sign-Speak Device Setup Real-time hand gesture recognition using TensorFlow

The Challenge

Individuals who rely on sign language often face communication barriers with those who do not understand it. The goal of this project was to create a portable, affordable, and highly accurate system that could translate sign language into spoken words in real-time.

The Solution

I developed a standalone device powered by a Raspberry Pi that acts as an intelligent interpreter:

  • Object Detection: Trained a custom Machine Learning model using the TensorFlow API to recognize specific hand signs and gestures.
  • Audio Integration: Integrated a Python text-to-speech library to vocalize the translated gestures instantly.
  • Hardware Deployment: Optimized the heavy machine learning models to run efficiently on a portable, low-power Raspberry Pi computer.

Technical Stack

  • Machine Learning: TensorFlow API, Computer Vision.
  • Programming: Python.
  • Hardware: Raspberry Pi, Camera Module.

Results & Impact

The system achieved an impressive 82% recognition accuracy across the trained gestures, with a 1.8-second response time from visual input to spoken output. This project solidified my passion for Machine Learning and demonstrated how advanced AI models can be deployed in the real world to create highly impactful, accessible technology.

[View Project on GitHub](https://github.com/Mohamed007k ML-sign-languages-gestures-detection-model)

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