Sign language recognition system via Kinect: Number and english alphabet

Wan Yi Yeh, Teng Hui Tseng, Jun Wei Hsieh, Chun Ming Tsai

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

4 Scopus citations

Abstract

The population of deaf-mute in Taiwan is increasing every year. These deaf-mute persons usually use sign language to communicate with each other. However, most hearing persons cannot understand sign languages because they have not learned it. In order to let normal persons understand sign language, a sign language recognition system is necessary. In this paper, such a sign language recognition system is proposed. In the proposed system, a depth image is captured by Kinect sensor, palm area is segmented from depth image, palm binary image is thresholded by using Otsu thresholding method, and background noises are removed by morphological closing operators, and SURF features and descriptors are extracted to identify the sign language. Experimental results show that the proposed method is effective to detect and identify the numerals and letters in the English Alphabet used in the sign language.

Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
PublisherIEEE Computer Society
Pages660-665
Number of pages6
ISBN (Electronic)9781509003891
DOIs
StatePublished - 2 Jul 2016
Event2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 - Jeju Island, Korea, Republic of
Duration: 10 Jul 201613 Jul 2016

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
CountryKorea, Republic of
CityJeju Island
Period10/07/1613/07/16

Keywords

  • Kinect sensor
  • Otsu thresholding
  • Sign language recognition
  • SURF feature and descriptor

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