Levenshtein distance for sign language datasets

This repository contains two Jupyter notebooks to allow the calculation of Levenshtein distances between two sign language datasets.

Alternatively you can use the google colab notebooks below:

All signs:

Only same glosses:

Instructions

Before using the notebooks make sure that your excel files contain all the necessary information like here:

There is no limit on how many parameters your files can have but make sure that your annotations are consistent.

In the scripts you must specify which column contains the glosses and which columns contain the annotations.

Prerequisites

You need to install the following python libraries in order to use the notebooks

pandas
csv
numpy
os
glob

Usage

Levenshtein distances for all signs

The notebook "Levenshtein_distances_all_signs.ipynb" will calculate how similar each gloss is in comparison to all the glosses of the second dataset.

1.0 means that the signs are identical and 0.0 completely different.

Its output will be like:

Levenshtein distances for same gloss signs

The notebook "Levenshtein_distances_only_same_glosses.ipynb" will calculate how similar each gloss is in comparison to only the same glosses of the second dataset.

Its output will be like:

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.