Colombian Sign Language Classification Based on Hands Pose and Machine Learning Techniques

Anny Vera, Camilo Pérez, Juan José Sánchez, Alvaro D. Orjuela-Cañón

Research output: Chapter in Book/ReportChapterResearch

Abstract

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.

Original languageEnglish (US)
Title of host publicationSmart Technologies, Systems and Applications - 3rd International Conference, SmartTech-IC 2022, Revised Selected Papers
EditorsFabián R. Narváez, Fernando Urgilés, Juan Pablo Salgado-Guerrero, Teodiano Freire Bastos-Filho
PublisherSpringer Science and Business Media Deutschland GmbH
Pages149-160
Number of pages12
ISBN (Print)9783031322129
DOIs
StatePublished - 2023
Event3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022 - Cuenca, Ecuador
Duration: Nov 16 2022Nov 18 2022

Publication series

NameCommunications in Computer and Information Science
Volume1705 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022
Country/TerritoryEcuador
CityCuenca
Period11/16/2211/18/22

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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