This thesis introduces an approach to obtain image-based hand features to accurately describe hand shapes commonly found in the American Sign Language. A hand recognition system capable of identifying 31 hand shapes from the American Sign Language was developed to identify hand shapes in a given input image or video sequence. An appearance-based approach with a single camera is used to recognize the hand shape. A region-based shape descriptor, the generic Fourier descriptor, invariant of translation, scale, and orientation, has been implemented to describe the shape of the hand. A wrist detection algorithm has been developed to remove the forearm from the hand region before the features are extracted. The recognition of the hand shapes is performed with a multi-class Support Vector Machine. Testing provided a recognition rate of approximately 84% based on widely varying testing set of approximately 1,500 images and training set of about 2,400 images. With a larger training set of approximately 2,700 images and a testing set of approximately 1,200 images, a recognition rate increased to about 88%.
Library of Congress Subject Headings
American Sign Language; Pattern recognition systems; Hand--Movements
Department, Program, or Center
Computer Engineering (KGCOE)
Rupe, Jonathan, "Vision-based hand shape identification for sign language recognition" (2005). Thesis. Rochester Institute of Technology. Accessed from
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