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Sign Language Recognizer Framework B...

Akandeh, Atra.

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Sign Language Recognizer Framework Based on Deep Learning Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sign Language Recognizer Framework Based on Deep Learning Algorithms./
Author:	Akandeh, Atra.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	110 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493306
ISBN:	9798544228547

Sign Language Recognizer Framework Based on Deep Learning Algorithms.
Akandeh, Atra.

Sign Language Recognizer Framework Based on Deep Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 110 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Michigan State University, 2021.

This item must not be sold to any third party vendors.

According to the World Health Organization (WHO, 2017), 5% of the world's population have hearing loss. Most people with hearing disabilities communicate via sign language, which hearing people find extremely difficult to understand. To facilitate communication of deaf and hard of hearing people, developing an efficient communication system is a necessity. There are many challenges associated with the Sign Language Recognition (SLR) task, namely, lighting conditions, complex background, signee body postures, camera position, occlusion, complexity and large variations in hand posture, no word alignment, coarticulation, etc.Sign Language Recognition has been an active domain of research since the early 90s. However, due to computational resources and sensing technology constraints, limited advancement has been achieved over the years. Existing sign language translation systems mostly can translate a single sign at a time, which makes them less effective in daily-life interaction. This work develops a novel sign language recognition framework using deep neural networks, which directly maps videos of sign language sentences to sequences of gloss labels by emphasizing critical characteristics of the signs and injecting domain-specific expert knowledge into the system. The proposed model also allows for combining data from variant sources and hence combating limited data resources in the SLR field.

ISBN: 9798544228547Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep Learning

Sign Language Recognizer Framework Based on Deep Learning Algorithms.
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500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Salem, Fathi.
502 $a Thesis (Ph.D.)--Michigan State University, 2021.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be sold to any third party vendors.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a According to the World Health Organization (WHO, 2017), 5% of the world's population have hearing loss. Most people with hearing disabilities communicate via sign language, which hearing people find extremely difficult to understand. To facilitate communication of deaf and hard of hearing people, developing an efficient communication system is a necessity. There are many challenges associated with the Sign Language Recognition (SLR) task, namely, lighting conditions, complex background, signee body postures, camera position, occlusion, complexity and large variations in hand posture, no word alignment, coarticulation, etc.Sign Language Recognition has been an active domain of research since the early 90s. However, due to computational resources and sensing technology constraints, limited advancement has been achieved over the years. Existing sign language translation systems mostly can translate a single sign at a time, which makes them less effective in daily-life interaction. This work develops a novel sign language recognition framework using deep neural networks, which directly maps videos of sign language sentences to sequences of gloss labels by emphasizing critical characteristics of the signs and injecting domain-specific expert knowledge into the system. The proposed model also allows for combining data from variant sources and hence combating limited data resources in the SLR field.
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