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The Viterbi Algorithm and Sign Language Recognition

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dc.contributor.advisor Dougherty, John P.
dc.contributor.author Perkoff, Elana
dc.date.accessioned 2016-01-25T16:52:46Z
dc.date.available 2016-01-25T16:52:46Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/10066/17660
dc.description.abstract This paper analyzes the Viterbi algorithm and its application to Sign Language Recognition. The Viterbi algorithm is used as a maximum a posteriori approach to solving the decoding problem of Hidden Markov Models (HMM). This paper discusses the attributes of the HMM with an example. The theoretical time complexity of the algorithm is compared to the results of experiments on a Python implementation. The more general field of Gesture Recognition is briefly mentioned as foundation for the type of system necessary to facilitate Sign Language Recognition. Both word and subunit models for American Sign Language are detailed.
dc.description.sponsorship Haverford College. Department of Computer Science
dc.language.iso eng
dc.rights.uri http://creativecommons.org/licenses/by-nc/3.0/us/
dc.subject.lcsh Gesture
dc.subject.lcsh American Sign Language
dc.subject.lcsh Pattern recognition systems
dc.subject.lcsh Hidden Markov models
dc.title The Viterbi Algorithm and Sign Language Recognition
dc.type Thesis
dc.rights.access Open Access


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http://creativecommons.org/licenses/by-nc/3.0/us/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/3.0/us/

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