Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comprehensive Study on Deep Learning-based Methods for Sign Language
  Recognition   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.

27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom