TY - GEN
T1 - Hand Gesture Recognition Methods and Applications
T2 - 7th International Conference on Engineering and MIS, ICEMIS 2021
AU - Zholshiyeva, Lazzat Zulpukharkyzy
AU - Zhukabayeva, Tamara Kokenovna
AU - Turaev, Sherzod
AU - Berdiyeva, Meruyert Aimambetovna
AU - Jambulova, Dina Tokhtasynovna
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/10/11
Y1 - 2021/10/11
N2 - Automatic Hand Gesture Recognition has become more important in recent years. Due to an increasing number of the deaf and hearing impaired, the use of a variety of non-contact-based applications and devices has also been increased. With the development of modern technology, it also plays a key role in the human-computer interaction systems. Sign language is the main instrument of communication among the deaf, the hearing impaired and the non-verbal. However, there are barriers for these groups in their daily interaction with people who do not understand any sign language. The recent studies in algorithm analysis and computer vision have led to the development of innovative efficient and accurate gesture recognition methods. Since HGR is the basis for sign language analysis, this paper is devoted to conducting the literary survey of hand gesture detection and recognition methods and algorithms, existing sign languages and their applications. The results of this paper can be summarized as following: artificial neural networks, which use advanced methods and algorithms for hand gesture detection and recognition, is the most useful classifier for the recognition of Kazakh Sign Language. In this paper, about 70 references published from 2012 to 2021 were reviewed for identifying common methods for hand gesture recognition.
AB - Automatic Hand Gesture Recognition has become more important in recent years. Due to an increasing number of the deaf and hearing impaired, the use of a variety of non-contact-based applications and devices has also been increased. With the development of modern technology, it also plays a key role in the human-computer interaction systems. Sign language is the main instrument of communication among the deaf, the hearing impaired and the non-verbal. However, there are barriers for these groups in their daily interaction with people who do not understand any sign language. The recent studies in algorithm analysis and computer vision have led to the development of innovative efficient and accurate gesture recognition methods. Since HGR is the basis for sign language analysis, this paper is devoted to conducting the literary survey of hand gesture detection and recognition methods and algorithms, existing sign languages and their applications. The results of this paper can be summarized as following: artificial neural networks, which use advanced methods and algorithms for hand gesture detection and recognition, is the most useful classifier for the recognition of Kazakh Sign Language. In this paper, about 70 references published from 2012 to 2021 were reviewed for identifying common methods for hand gesture recognition.
KW - Computer vision
KW - Deep learning
KW - Feature extraction
KW - Hand gesture recognition
KW - Sign language
UR - http://www.scopus.com/inward/record.url?scp=85121996094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121996094&partnerID=8YFLogxK
U2 - 10.1145/3492547.3492578
DO - 10.1145/3492547.3492578
M3 - Conference contribution
AN - SCOPUS:85121996094
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 7th International Conference on Engineering and MIS, ICEMIS 2021
PB - Association for Computing Machinery
Y2 - 11 October 2021 through 13 October 2021
ER -