Finger spelling recognition using neural network

Kian Ming Lim, Kok Seang Tan, Alan W.C. Tan, Shing Chiang Tan, Chin Poo Lee, Siti Fatimah Abdul Razak

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

Abstract

Finger spelling is a way of communication by expressing words using hand signs in order to ensure deaf and dumb community can communicate with others effectively. Therefore, a system that can understand finger spelling is needed. As a result of that, this work is conducted to primarily develop a tutoring system for finger spelling. To develop a robust real-time finger spelling tutoring system, it is necessary to ensure the accuracy of the finger spelling recognition. Even though there are existing solutions available for a decade, but most of them are just focusing on improving accuracy rate without implementing their solutions as a complete tutoring system for finger spelling. Consequently, it inspires this research project to develop a tutoring system for finger spelling. Microsoft Kinect sensor is used to acquire color images and depth images of the finger spells. Depth images are used to perform segmentation on the color images. After that, the segmented images are used as input and pass into a two hidden layers backpropagation neural network for classification.

Original languageEnglish
Title of host publication2015 IEEE Student Conference on Research and Development, SCOReD 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages78-81
Number of pages4
ISBN (Electronic)9781467395724
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE Student Conference on Research and Development, SCOReD 2015 - Kuala Lumpur, Malaysia
Duration: 13 Dec 201514 Dec 2015

Publication series

Name2015 IEEE Student Conference on Research and Development, SCOReD 2015

Conference

ConferenceIEEE Student Conference on Research and Development, SCOReD 2015
Country/TerritoryMalaysia
CityKuala Lumpur
Period13/12/1514/12/15

Keywords

  • backpropagation neural network
  • finger spelling recognition
  • Miscrosoft Kinect sensor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology
  • Computer Networks and Communications
  • Computer Science Applications

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