Automated sign language alphabet detection

Ashwin Van Der Merwe, Elie Ngomseu Mambou, Theo G. Swart

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The possibility of using sensor-based data acquisition to supply the necessary information to an Artificial Neural Network (ANN), for South African Sign Language (SASL) alphabet recognition, is investigated. To accomplish this, a data glove was designed and implemented to generate the necessary sensory information. Thereafter, training the ANN to recognize a selection of signs in the SASL alphabet was achieved. The system was able to recognize six signs with an accuracy of 96%. This result provides a foundation for a system that will enable bilateral communication between the deaf and hearing demographic to mitigate the communication hindrance.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE AFRICON, AFRICON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419840
Publication statusPublished - 13 Sept 2021
Event2021 IEEE AFRICON, AFRICON 2021 - Virtual, Arusha, Tanzania, United Republic of
Duration: 13 Sept 202115 Sept 2021

Publication series

NameIEEE AFRICON Conference
ISSN (Print)2153-0025
ISSN (Electronic)2153-0033


Conference2021 IEEE AFRICON, AFRICON 2021
Country/TerritoryTanzania, United Republic of
CityVirtual, Arusha


  • Artificial Neural Network (ANN)
  • Automated Sign Detection
  • Data Glove
  • Machine Learning
  • South African Sign Language (SASL)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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