TY - CHAP
T1 - Colombian Sign Language Classification Based on Hands Pose and Machine Learning Techniques
AU - Vera, Anny
AU - Pérez, Camilo
AU - Sánchez, Juan José
AU - Orjuela-Cañón, Alvaro D.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - New technologies can improve the inclusion of deaf (and hearing loss) people in different scenarios. In the present work, a classification of the Colombian sign language alphabet was implemented. For this, the employment of the media-pipe hands pose tool was used to feature extraction process. Then, three machine learning models: support vector classifiers, artificial neural networks and random forest, were trained to determine the best proposal. Results show how a neural network with one hidden layer obtained the best performance with 99.41%. The support vector classifier reached an accuracy of 99.12%, and the worse result was achieved by the random forest model with 96.67% in the classification. The proposal can contribute with advances in the sign language recognition in the Colombian context, which has been worked in different approaches with more complex models to do similar classifications.
AB - New technologies can improve the inclusion of deaf (and hearing loss) people in different scenarios. In the present work, a classification of the Colombian sign language alphabet was implemented. For this, the employment of the media-pipe hands pose tool was used to feature extraction process. Then, three machine learning models: support vector classifiers, artificial neural networks and random forest, were trained to determine the best proposal. Results show how a neural network with one hidden layer obtained the best performance with 99.41%. The support vector classifier reached an accuracy of 99.12%, and the worse result was achieved by the random forest model with 96.67% in the classification. The proposal can contribute with advances in the sign language recognition in the Colombian context, which has been worked in different approaches with more complex models to do similar classifications.
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UR - https://www.mendeley.com/catalogue/83128145-4759-350a-861d-dd8379241d7c/
U2 - 10.1007/978-3-031-32213-6_11
DO - 10.1007/978-3-031-32213-6_11
M3 - Chapter
AN - SCOPUS:85161253974
SN - 9783031322129
T3 - Communications in Computer and Information Science
SP - 149
EP - 160
BT - Smart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers
A2 - Narváez, Fabián R.
A2 - Urgilés, Fernando
A2 - Salgado-Guerrero, Juan Pablo
A2 - Bastos-Filho, Teodiano Freire
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022
Y2 - 16 November 2022 through 18 November 2022
ER -
