Real-Time Sign Language Recognition

Cristian Amaya, Victor Murray

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

2 Scopus citations

Abstract

We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80% and an execution time of 59 milliseconds per frame.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728193779
DOIs
StatePublished - Sep 2020
Externally publishedYes
Event27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Peru
Duration: 3 Sep 20205 Sep 2020

Publication series

NameProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Conference

Conference27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Country/TerritoryPeru
CityVirtual, Lima
Period3/09/205/09/20

Keywords

  • Principal component analysis
  • sign language recognition
  • support vector machines

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