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

• URL: http://arxiv.org/abs/2007.12530v2
• Date: Fri, 19 Mar 2021 19:32:15 GMT
• Title: A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
• Authors: Nikolas Adaloglou, Theocharis Chatzis, Ilias Papastratis, Andreas Stergioulas, Georgios Th. Papadopoulos, Vassia Zacharopoulou, George J. Xydopoulos, Klimnis Atzakas, Dimitris Papazachariou, and Petros Daras
• Abstract summary: The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. 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.
• Score: 14.714669469867871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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