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sign-language-recognition's Introduction

Sign Language Recognition

Recognize American Sign Language (ASL) using Machine Learning.
Currently, the following algorithms are supported:

The training images were retrieved from a video, filmed at 640x480 resolution using a mobile camera.

Dependencies:

  • OpenCV 2.4.11, for image-processing.
  • Scikit-learn 0.18.1, for machine-learning algorithms.

Usage:

For training a dataset of your own, do the following steps:

  1. Put all the training and testing images in a directory and update their paths in the config file code/common/config.py. Optionally, you can generate the images in real-time from webcam - capture_from_camera.py.
  2. Generate image-vs-label mapping fsor all the training images - generate_images_labels.py train.
  3. Apply the image-transformation algorithms to the training images - transform_images.py.
  4. Train the model - train_model.py <model-name>.
  5. Generate image-vs-label mapping for all the test images - generate_images_labels.py test.
  6. Test the model - predict_from_file.py <model-name>. Optionally, you can test the model on a live video stream from a webcam - predict_from_camera.py.

A sample workflow can be seen in run.sh.

However, if you wish not to use your own dataset, you can skip some of these steps and use the pre-trained models trained using this dataset:

  1. Download and replace the contents of the directory data/generated from here. It contains the serialized model files, the transformed images as well as the image-vs-label mapping files.
  2. Test the model - predict_from_file.py <model-name>.

A sample workflow can be seen in run_quick.sh.

To-Do:

  • Improve the command-line-arguments input mechanism.
  • Add progress bar while transforming images.
  • Add logger.

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