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Sign Language Detection

This project uses a classic machine learning approach to detect the sign language from the hand gestures by using the webcamera. The goal is to build a system that can remove the communication gap for the people with hearing and speech difficulties.

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Features

• Real-time gesture detection using a webcam.
• Detects custom hand gestures (trained on your own dataset).
• Displays the recognized gesture.
• Uses MediaPipe for accurate hand tracking.
• Built with TensorFlow for Machine Learning predictions.

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Dataset

This project uses my own dataset of hand gestures. I collected the hand gestures(1385 images of total 5 categories) using my web camera and trained them using google teachable machine. After that i exported them as keras model and use them.

• Source: Dataset Google Drive
• File: keras_model.h5

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Requirements

Library / Tool	Version
Python	3.11.7
TensorFlow	2.12.1
MediaPipe	0.10.18
OpenCV-Python	4.10.0.84
NumPy	latest
CVZone	latest

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Installation

1. Clone the repository:

git clone https://github.com/Pranto-Bapary/Sign-Language-Detection.git
cd sign-language-detection

2. Create a virtual environment:

python -m venv venv

3. Activate the virtual environment:

# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

4. Upgrade pip:

pip install --upgrade pip

5. Install required packages:

pip install tensorflow==2.12.1 mediapipe==0.10.18 opencv-python==4.10.0.84 numpy cvzone

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Workflow

The project follows a real-time machine learning pipeline for sign language detection:

• Data Collection & Model Preparation:

  • The TensorFlow model is trained on custom hand gesture images collected specifically for this project.
  • Each gesture (e.g., "Thank You", "Hello") has multiple images for better generalization.

• Webcam Input & Hand Detection:

  • The application accesses the webcam using OpenCV.
  • MediaPipe's Hand Tracking module detects and tracks hand landmarks in real-time.

• Feature Extraction & Preprocessing:

  • Hand landmarks are extracted as keypoints (x, y coordinates).
  • Keypoints are normalized and reshaped to match the TensorFlow model input format.

• Gesture Prediction:

  • Pre-processed features are fed into the TensorFlow model.
  • The model predicts the gesture and outputs a confidence percentage for each gesture.

• Output Display:

  • The predicted gesture is displayed on the screen along with the confidence score (e.g., "Thank You – 92%").
  • Real-time feedback allows users to see gestures detected live as they perform them.

• Optional Enhancements:

  • Detect multiple gestures in sequence.
  • Apply thresholds to filter low-confidence predictions.

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Usage

1. Activate your virtual environment.
2. Run the test Python script:

python test.py

3. Perform hand gestures in front of the webcam.
4. The predicted gesture and confidence will be displayed in real-time.

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