Design and development of phoneme based sign language recognition system for the hearing impaired

Sign language recognition is one of the most promising sub-fields in gesture recognition research. Sign languages are commonly developed for hearing impaired communities, which can include interpreters, friends and families of hearing impaired people as well...

Full description

Saved in:

Bibliographic Details
Main Author:	Rajkumar, Palaniappan
Format:	Thesis
Language:	English
Subjects:	Sign language recognition Hand gesture Gesture recognition Artificial Neural Network (ANN) Sign languages Hearing impaired
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/2/Full%20text.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-31948
record_format	uketd_dc
spelling	my-unimap-319482014-02-15T02:45:27Z Design and development of phoneme based sign language recognition system for the hearing impaired Rajkumar, Palaniappan Sign language recognition is one of the most promising sub-fields in gesture recognition research. Sign languages are commonly developed for hearing impaired communities, which can include interpreters, friends and families of hearing impaired people as well as people who are hard of hearing themselves. This thesis discusses the development of a Phoneme based sign language recognition system for the hearing impaired. Previous research on sign language recognition systems have concentrated on finger spellings recognition or isolated word recognition. This research focuses on developing a sign language recognition system for recognizing 44 English phonemes. To represent the 44 English phonemes, as a first step, 11 different gestures were developed. By selecting suitable combination of these 11 gestures for the right and left hand, 44 different gesture combinations were formulated. The signed data are collected from seven subjects using an ordinary web camera at a resolution of 640×480 pixels. The data is preprocessed and features are extracted from the segmented regions of the signed data. A newly proposed interleaving preprocessing algorithm used in developing the sign language recognition system is discussed in this thesis. Artificial Neural Network (ANN) provides alternative form of computing that attempts to mimic the functionality of the brain. The feature set is then feed to the neural network model to classify the phoneme sign. An audio system is installed to play the particular word for the communication between the ordinary people and hearing impaired community. Experimental results show that the use of proposed interleaving method yields a better classification accuracy compared to the conventional method. The vertical interleaving method using combined blur and affine moment invariant features and Elman network yields the maximum classification accuracy of 95.50%. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/31948 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/1/Page%201-24.pdf c087ebe86164f1d672f681342e4321ed http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/2/Full%20text.pdf e35a296d7274baf1e367718976c8be1e http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Sign language recognition Hand gesture Gesture recognition Artificial Neural Network (ANN) Sign languages Hearing impaired School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Sign language recognition Hand gesture Gesture recognition Artificial Neural Network (ANN) Sign languages Hearing impaired
spellingShingle	Sign language recognition Hand gesture Gesture recognition Artificial Neural Network (ANN) Sign languages Hearing impaired Rajkumar, Palaniappan Design and development of phoneme based sign language recognition system for the hearing impaired
description	Sign language recognition is one of the most promising sub-fields in gesture recognition research. Sign languages are commonly developed for hearing impaired communities, which can include interpreters, friends and families of hearing impaired people as well as people who are hard of hearing themselves. This thesis discusses the development of a Phoneme based sign language recognition system for the hearing impaired. Previous research on sign language recognition systems have concentrated on finger spellings recognition or isolated word recognition. This research focuses on developing a sign language recognition system for recognizing 44 English phonemes. To represent the 44 English phonemes, as a first step, 11 different gestures were developed. By selecting suitable combination of these 11 gestures for the right and left hand, 44 different gesture combinations were formulated. The signed data are collected from seven subjects using an ordinary web camera at a resolution of 640×480 pixels. The data is preprocessed and features are extracted from the segmented regions of the signed data. A newly proposed interleaving preprocessing algorithm used in developing the sign language recognition system is discussed in this thesis. Artificial Neural Network (ANN) provides alternative form of computing that attempts to mimic the functionality of the brain. The feature set is then feed to the neural network model to classify the phoneme sign. An audio system is installed to play the particular word for the communication between the ordinary people and hearing impaired community. Experimental results show that the use of proposed interleaving method yields a better classification accuracy compared to the conventional method. The vertical interleaving method using combined blur and affine moment invariant features and Elman network yields the maximum classification accuracy of 95.50%.
format	Thesis
author	Rajkumar, Palaniappan
author_facet	Rajkumar, Palaniappan
author_sort	Rajkumar, Palaniappan
title	Design and development of phoneme based sign language recognition system for the hearing impaired
title_short	Design and development of phoneme based sign language recognition system for the hearing impaired
title_full	Design and development of phoneme based sign language recognition system for the hearing impaired
title_fullStr	Design and development of phoneme based sign language recognition system for the hearing impaired
title_full_unstemmed	Design and development of phoneme based sign language recognition system for the hearing impaired
title_sort	design and development of phoneme based sign language recognition system for the hearing impaired
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31948/2/Full%20text.pdf
_version_	1747836797876961280

Design and development of phoneme based sign language recognition system for the hearing impaired

Similar Items