South African sign language recognition using feature vectors and Hidden Markov Models
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Date
2010
Authors
Journal Title
Journal ISSN
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Publisher
University of the Western Cape
Abstract
This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.
Description
Masters of Science
Keywords
Optical pattern recognition, Mathematical models, Image processing, Digital techniques, Markov processes