Features & Objectives

Key Features

• Real-Time Gesture Recognition:
  Leverages OpenCV and cvzone for hand detection and tracking with minimal latency.

• Deep Learning-Based Classification:
  Uses a CNN (pretrained model: trainedModel.h5) for accurate classification of sign language gestures.

• Dual Output:
  Provides immediate text display and synthesized speech via the pyttsx3 text-to-speech engine.

• User-Friendly Interface:
  An intuitive GUI built with Tkinter displays the live camera feed, recognized characters, and word suggestions.

Objectives

• Accurate Conversion:
  Develop a system that reliably translates sign language gestures into text and audible speech.

• Enhanced Accessibility:
  Empower individuals with hearing and speech impairments by facilitating real-time communication without the need for human interpreters.

• Modular and Scalable Design:
  Ensure that the system is maintainable, with clear modules for gesture recognition, text processing, and UI management.

• Performance Optimization:
  Achieve high accuracy and responsiveness under diverse lighting and environmental conditions.