Vision-based hand shape identification for sign language recognition

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dc.contributor.advisor Cockburn, Juan en_US
dc.contributor.advisor Savakis, Andreas en_US
dc.contributor.advisor Canosa, Roxanne en_US
dc.contributor.author Rupe, Jonathan en_US
dc.date.accessioned 2005-06-30T18:06:29Z en_US
dc.date.accessioned 2006-03-07T19:43:24Z en_US
dc.date.available 2005-06-30T18:06:29Z en_US
dc.date.available 2006-03-07T19:43:24Z en_US
dc.date.issued 2005-06-30T18:06:29Z en_US
dc.identifier.uri http://hdl.handle.net/1850/940 en_US
dc.description.abstract 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%. en_US
dc.format.extent 25781 bytes en_US
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dc.language.iso en_US en_US
dc.subject Feature extraction en_US
dc.subject Generic fourier descriptor en_US
dc.subject Gesture recognition en_US
dc.subject Hand posture estimation en_US
dc.subject Hand shape estimation en_US
dc.subject Hand shape identification en_US
dc.subject Hand shape recognition en_US
dc.subject Sign language recognition en_US
dc.subject Wrist detection en_US
dc.subject.lcc HV2474 .R86 2005 en_US
dc.subject.lcsh American Sign Language en_US
dc.subject.lcsh Pattern recognition systems en_US
dc.subject.lcsh Hand--Movements en_US
dc.title Vision-based hand shape identification for sign language recognition en_US
dc.type Thesis en_US
dc.description.college Kate Gleason College of Engineering en_US
dc.description.department Computer Engineering en_US
dc.description.approval 2005-04 en_US

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