  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.
