Skip to content

dscha09/Sign-Language-Recognition

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 

Repository files navigation

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.

About

✌️ 👌 ✊ 📷 Sign Language Recognition using Python

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Python 94.1%
  • Shell 5.9%