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

Abstract

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%.