Sign Language Recognition

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

• K-Nearest-Neighbours
• Logistic Regression
• Support Vector Machines

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

Setup:

• Install Python3 (last tested on Python3.7).
• Install pipenv.
• In the project root directory, execute pipenv sync.

Usage:

You can directly start classifying new images using the pre-trained models (the .pkl files in data/generated/output/<model_name>/) trained using the above dataset:

  python predict_from_file.py <model-name>

Note that the pre-generated model files do not contain the file for knn due to its large size.
If you want to use knn, then download it separately from here and place it in data/generated/output/knn/.
The models available by default are svm and logistic.

The above workflow can be executed using run_quick.sh.

However, if you wish to use your own dataset, 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.
   (Or skip to use the default paths which should also work).
   Optionally, you can generate the images in real-time from webcam - python capture_from_camera.py.
2. Generate image-vs-label mappings for all the training images - python generate_images_labels.py train.
3. Apply the image-transformation algorithms to the training images - python transform_images.py.
4. Train the model - python train_model.py <model-name>. Model names can be svm/knn/logistic.
5. Generate image-vs-label mapping for all the test images - python generate_images_labels.py test.
6. Test the model - python predict_from_file.py <model-name>.
   Optionally, you can test the model on a live video stream from a webcam - python predict_from_camera.py.
   (If recording, then make sure to have the same background and hand alignment as in the training images.)

All the python commands above have to be executed from the code/ directory.
The above workflow can be executed using run.sh.

To-Do:

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