Standard

Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM. / Grif, M. G.; Kondratenko, Y. K.

в: Journal of Physics: Conference Series, Том 2032, № 1, 012024, 18.10.2021.

Результаты исследований: Научные публикации в периодических изданиях › статья по материалам конференции › Рецензирование

Harvard

Grif, MG & Kondratenko, YK 2021, 'Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM', Journal of Physics: Conference Series, Том. 2032, № 1, 012024. https://doi.org/10.1088/1742-6596/2032/1/012024

APA

Grif, M. G., & Kondratenko, Y. K. (2021). Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM. Journal of Physics: Conference Series, 2032(1), [012024]. https://doi.org/10.1088/1742-6596/2032/1/012024

Vancouver

Grif MG, Kondratenko YK. Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM. Journal of Physics: Conference Series. 2021 окт. 18;2032(1):012024. doi: 10.1088/1742-6596/2032/1/012024

Author

Grif, M. G. ; Kondratenko, Y. K. / Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM. в: Journal of Physics: Conference Series. 2021 ; Том 2032, № 1.

BibTeX

@article{662f19ce68df4efda7500b2606f13388,
title = "Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM",
abstract = "Russian Sign Language is a natural language that serves as a means of communication for people with hearing impairments. Currently, it is necessary to provide communication paths between hearing and deaf people, which requires the solution of several specific problems, one of which is gesture recognition. The paper presents a system for recognizing isolated static and dynamic gestures of the dactyl alphabet of the Russian Sign Language. The system is based on machine learning methods and is an LSTM with Mediapipe Hands as feature extractor. The network showed an F-measure value of 91% on test data.",
author = "Grif, {M. G.} and Kondratenko, {Y. K.}",
note = "Funding Information: This paper was financially supported by the Russian Foundation for Basic Research (Grant No. 19-57-45006). Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 2021 International Conference on IT in Business and Industry, ITBI 2021 ; Conference date: 12-05-2021 Through 14-05-2021",
year = "2021",
month = oct,
day = "18",
doi = "10.1088/1742-6596/2032/1/012024",
language = "English",
volume = "2032",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Development of a software module for recognizing the fingerspelling of the Russian Sign Language based on LSTM

AU - Grif, M. G.

AU - Kondratenko, Y. K.

N1 - Funding Information: This paper was financially supported by the Russian Foundation for Basic Research (Grant No. 19-57-45006). Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved.

PY - 2021/10/18

Y1 - 2021/10/18

N2 - Russian Sign Language is a natural language that serves as a means of communication for people with hearing impairments. Currently, it is necessary to provide communication paths between hearing and deaf people, which requires the solution of several specific problems, one of which is gesture recognition. The paper presents a system for recognizing isolated static and dynamic gestures of the dactyl alphabet of the Russian Sign Language. The system is based on machine learning methods and is an LSTM with Mediapipe Hands as feature extractor. The network showed an F-measure value of 91% on test data.

AB - Russian Sign Language is a natural language that serves as a means of communication for people with hearing impairments. Currently, it is necessary to provide communication paths between hearing and deaf people, which requires the solution of several specific problems, one of which is gesture recognition. The paper presents a system for recognizing isolated static and dynamic gestures of the dactyl alphabet of the Russian Sign Language. The system is based on machine learning methods and is an LSTM with Mediapipe Hands as feature extractor. The network showed an F-measure value of 91% on test data.

UR - http://www.scopus.com/inward/record.url?scp=85118462854&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2032/1/012024

DO - 10.1088/1742-6596/2032/1/012024

M3 - Conference article

AN - SCOPUS:85118462854

VL - 2032

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012024

T2 - 2021 International Conference on IT in Business and Industry, ITBI 2021

Y2 - 12 May 2021 through 14 May 2021

ER -

ID: 34605337