Construction and Evaluation of a Dynamic Sign Dataset for the Colombian Sign Language

Nowadays the amount of people that is involved in some degree of hearing loss is increasing, and sign languages are useful as a rehabilitation method when there is a complete loss of hearing. However, countries like Colombia where there is not enough infrastructure and interpreters, the teaching of a sign language is affected. On the other hand, start with the learning of basic vocabulary of a sign language is a key factor for building the ability of making sentences. The first signs that are usually learned are: alphabet, colors, numbers, family members, pronouns, and greetings. This is where Computer Vision have brought tools for the translation of various signs. This allows to the development of solutions that reduce the frequent need of interpreters when learning basic vocabulary. This work proposes the creation of a word-based dataset for the signs of colors in the Colombian Sign Language. It was evaluated with three different Neural Network architectures: 1) LSTM, 2) GRU and 3) Conv_2D. Where the input for the network were the landmark coordinates extracted with MediaPipe. On the other hand, the Neural Network was evaluated with a dataset split of 80 % for training and 20 % for testing. Finally, it was seen a higher performance for the architecture based on LSTM, having a precision on detection of 92.6 %. The results, show the importance of Machine Learning tools that consider time-dependent data for developing solutions that support self-teaching on dynamic signs.