Machine Learning Models Applied in Sign Language Recognition

Esteban Gustavo Novillo Quinde; Juan Pablo Saldaña Torres; Michael Andres Alvarez Valdez; John Santiago Llivicota Leon; Remigio Ismael Hurtado Ortiz

doi:10.1007/978-3-031-33783-3_25

Machine Learning Models Applied in Sign Language Recognition

Esteban Gustavo Novillo Quinde, Juan Pablo Saldaña Torres, Michael Andres Alvarez Valdez, John Santiago Llivicota Leon, Remigio Ismael Hurtado Ortiz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

One of the most relevant worldwide problems is the inclusion of people with disabilities. In this research we want to help focusing in the people with hearing disabilities, being able to translate sign language into words that we could read. It is a common worldwide problem to be able to accurately predict the gestures of non-hearing people in order to be able to communicate efficiently with them and not have a barrier when they want to perform their daily activities. In order to that we propose a three phase method combining Data preparation(The dataset used for this is the “Australian Sign Language sings”, which is public and free to use) and cleaning phase, modeling using Random Forest Vector Support Machine and Neural Networks, able to optimize and qualify these models using the measures of accuracy, precision, recall and f1-score. Therefore, in this work we try to offer the highest possible quality measures to the prediction of signs in the Australian language with the mentioned dataset. This also opens the way for future research where more advanced supervised modeling techniques can be applied to improve the values obtained.

Original language	English
Title of host publication	Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings
Editors	Ansel Yoan Rodríguez-González, Humberto Pérez-Espinosa, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	263-272
Number of pages	10
ISBN (Print)	9783031337826
DOIs	https://doi.org/10.1007/978-3-031-33783-3_25
State	Published - 2023
Event	15th Mexican Conference on Pattern Recognition, MCPR 2023 - Tepic, Mexico Duration: 21 Jun 2023 → 24 Jun 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13902 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Mexican Conference on Pattern Recognition, MCPR 2023
Country/Territory	Mexico
City	Tepic
Period	21/06/23 → 24/06/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Data science
Machine Learning
Neural Network
Random Forest
Sign Language
Vector Support Machine

Access to Document

10.1007/978-3-031-33783-3_25

Cite this

Novillo Quinde, E. G., Saldaña Torres, J. P., Alvarez Valdez, M. A., Llivicota Leon, J. S., & Hurtado Ortiz, R. I. (2023). Machine Learning Models Applied in Sign Language Recognition. In A. Y. Rodríguez-González, H. Pérez-Espinosa, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, & J. A. Olvera-López (Eds.), Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings (pp. 263-272). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13902 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33783-3_25

Novillo Quinde, Esteban Gustavo ; Saldaña Torres, Juan Pablo ; Alvarez Valdez, Michael Andres et al. / Machine Learning Models Applied in Sign Language Recognition. Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings. editor / Ansel Yoan Rodríguez-González ; Humberto Pérez-Espinosa ; José Francisco Martínez-Trinidad ; Jesús Ariel Carrasco-Ochoa ; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2023. pp. 263-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Machine Learning Models Applied in Sign Language Recognition",

abstract = "One of the most relevant worldwide problems is the inclusion of people with disabilities. In this research we want to help focusing in the people with hearing disabilities, being able to translate sign language into words that we could read. It is a common worldwide problem to be able to accurately predict the gestures of non-hearing people in order to be able to communicate efficiently with them and not have a barrier when they want to perform their daily activities. In order to that we propose a three phase method combining Data preparation(The dataset used for this is the “Australian Sign Language sings”, which is public and free to use) and cleaning phase, modeling using Random Forest Vector Support Machine and Neural Networks, able to optimize and qualify these models using the measures of accuracy, precision, recall and f1-score. Therefore, in this work we try to offer the highest possible quality measures to the prediction of signs in the Australian language with the mentioned dataset. This also opens the way for future research where more advanced supervised modeling techniques can be applied to improve the values obtained.",

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Novillo Quinde, EG, Saldaña Torres, JP, Alvarez Valdez, MA, Llivicota Leon, JS & Hurtado Ortiz, RI 2023, Machine Learning Models Applied in Sign Language Recognition. in AY Rodríguez-González, H Pérez-Espinosa, JF Martínez-Trinidad, JA Carrasco-Ochoa & JA Olvera-López (eds), Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13902 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 263-272, 15th Mexican Conference on Pattern Recognition, MCPR 2023, Tepic, Mexico, 21/06/23. https://doi.org/10.1007/978-3-031-33783-3_25

Machine Learning Models Applied in Sign Language Recognition. / Novillo Quinde, Esteban Gustavo; Saldaña Torres, Juan Pablo; Alvarez Valdez, Michael Andres et al.
Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings. ed. / Ansel Yoan Rodríguez-González; Humberto Pérez-Espinosa; José Francisco Martínez-Trinidad; Jesús Ariel Carrasco-Ochoa; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2023. p. 263-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13902 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Novillo Quinde EG, Saldaña Torres JP, Alvarez Valdez MA, Llivicota Leon JS, Hurtado Ortiz RI. Machine Learning Models Applied in Sign Language Recognition. In Rodríguez-González AY, Pérez-Espinosa H, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA, editors, Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 263-272. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-33783-3_25