An intelligent gesture recognition system

Information and knowledge are expanding in quantity and accessibility. However, people with functional limitations, such as hearing impaired, often left out of conversation where there are wide communication gaps between them with the ordinary people. The sign language is the fundamental communic...

Full description

Saved in:

Bibliographic Details
Main Author:	Wan Mohd Ridzuan, Wan Ab Majid
Format:	Thesis
Language:	English
Subjects:	Artificial intelligence Detectors Gesture recognition Hearing impaired Sign languages
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/2/Full%20text.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-33130
record_format	uketd_dc
spelling	my-unimap-331302014-03-26T03:40:08Z An intelligent gesture recognition system Wan Mohd Ridzuan, Wan Ab Majid Information and knowledge are expanding in quantity and accessibility. However, people with functional limitations, such as hearing impaired, often left out of conversation where there are wide communication gaps between them with the ordinary people. The sign language is the fundamental communication method between people who suffer from hearing defects. In order for an ordinary people to communicate with hearing impaired community, a translator is usually needed to translate the sign language into natural language. This project presents a simple method for converting sign language into voice signal using features obtained from the hand gestures. Using a camera, the system receives sign language video from the hearing impaired subject in the form of video streams in RGB (red-green-blue) colour with a screen bit depth of 24-bits and a resolution of 320 x 240 pixels. For each frame of images, two hand regions are segmented and then converted into binary image. Feature extraction model is then applied on each of segmented image to get the most important feature from the image. Artificial Neural Network (ANN) provides alternative form of computing that attempts to mimic the functionality of the brain. A simple neural network model is developed for sign recognition directly from the video stream. An audio system is installed to play the particular word for the communication between the ordinary people and hearing impaired community. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/33130 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/1/Page%201-24.pdf fcec3c84570cd340167dddafed1cb54f http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/2/Full%20text.pdf 34717dd7500b4c6248ca7c10557c19b6 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Artificial intelligence Detectors Gesture recognition Hearing impaired Sign languages School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Artificial intelligence Detectors Gesture recognition Hearing impaired Sign languages
spellingShingle	Artificial intelligence Detectors Gesture recognition Hearing impaired Sign languages Wan Mohd Ridzuan, Wan Ab Majid An intelligent gesture recognition system
description	Information and knowledge are expanding in quantity and accessibility. However, people with functional limitations, such as hearing impaired, often left out of conversation where there are wide communication gaps between them with the ordinary people. The sign language is the fundamental communication method between people who suffer from hearing defects. In order for an ordinary people to communicate with hearing impaired community, a translator is usually needed to translate the sign language into natural language. This project presents a simple method for converting sign language into voice signal using features obtained from the hand gestures. Using a camera, the system receives sign language video from the hearing impaired subject in the form of video streams in RGB (red-green-blue) colour with a screen bit depth of 24-bits and a resolution of 320 x 240 pixels. For each frame of images, two hand regions are segmented and then converted into binary image. Feature extraction model is then applied on each of segmented image to get the most important feature from the image. Artificial Neural Network (ANN) provides alternative form of computing that attempts to mimic the functionality of the brain. A simple neural network model is developed for sign recognition directly from the video stream. An audio system is installed to play the particular word for the communication between the ordinary people and hearing impaired community.
format	Thesis
author	Wan Mohd Ridzuan, Wan Ab Majid
author_facet	Wan Mohd Ridzuan, Wan Ab Majid
author_sort	Wan Mohd Ridzuan, Wan Ab Majid
title	An intelligent gesture recognition system
title_short	An intelligent gesture recognition system
title_full	An intelligent gesture recognition system
title_fullStr	An intelligent gesture recognition system
title_full_unstemmed	An intelligent gesture recognition system
title_sort	intelligent gesture recognition system
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33130/2/Full%20text.pdf
_version_	1747836802853502976

An intelligent gesture recognition system

Similar Items