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SignWave

SignWave is a program designed to detect American Sign Language (ASL) hand gestures in real-time. It leverages machine learning algorithms and computer vision techniques to detect hand gesture to character translations via webcam. It features capabilities for gathering training data, constructing image data sets, training classifiers, and conducting live recognition of ASL characters. This was developed using Python, OpenCV, MediaPipe, and scikit-learn.

Files

  1. image_collector.py
    • Python script to capture ASL character training images from the camera
  2. dataset_generator.py
    • Script to generate image datasets for training from collected images
  3. image_classifier.py
    • Trains the classifier to recognize ASL hand images
  4. main.py
    • Main program for detecting live ASL hand gestures

Usage

  1. Image Collection
    • Run image_collector.py to collect ASL character training images from the camera. Images are saved in corresponding directories in the ./data directory.
  2. Dataset Generation
    • Execute dataset_generator.py to generate image datasets for training from the collected images. This dataset will be saved in data.pickle.
  3. Training
    • Run image_classifier.py to train the classifier on the generated datasets. The trained model is saved as model.p.
  4. Live Detection
    • Execute main.py to detect ASL hand gestures in real-time using the webcam.

Requirements

  • Python 3.xx
  • OpenCV (pip install opencv-python)
  • MediaPipe (pip install mediapipe)
  • scikit-learn (pip install scikit-learn)