SUPERVISED AND WEAKLY-SUPERVISED CLASSIFICATION OF HANDSHAPES IN RUSSIAN SIGN LANGUAGE

Abstract

Creating automatically handshape classification inventory is a time-consuming process, in view of the fact that handshape datasets have to be carefully classified by linguists. Thus, only some of the popular languages have such handshape automatic classification inventory. This project aims to create a strong algorithm to classify a large unlabeled dataset of sign language handshapes. Previous works in image classification show significant results of more than 80% accuracy, but there are no relevant sources that echoed the same results in the classification of large weakly-labeled handshapes dataset using sem-supervised learning. As it was mentioned previously, the dataset is one of the main problems in that theme. In this work, we have a large set of unlabeled samples and about 45 classes of labeled image samples. Therefore, the selected approach should work well even when labeled data are not abundant. It is planned to test the semi-supervised learning approaches that take advantage of the small but labeled set such as Noisy-Student Training and it s expected to outperform results of the supervised Deep Hand model on the same dataset.