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arXiv · 2007.12530

A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition

Nikolas Adaloglou·Theocharis Chatzis·Ilias Papastratis·Andreas Stergioulas·Georgios Th. Papadopoulos·Vassia Zacharopoulou·George J. Xydopoulos·Klimnis Atzakas·Dimitris Papazachariou·Petros Daras
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Abstract

In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes is thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for a video capture.

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Nikolas Adaloglou, Theocharis Chatzis, Ilias Papastratis, Andreas Stergioulas, Georgios Th. Papadopoulos, Vassia Zacharopoulou, George J. Xydopoulos, Klimnis Atzakas, Dimitris Papazachariou, Petros Daras. 2021-03-19. A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition. https://doi.org/10.1109/tmm.2021.3070438

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