Design of Sensory Gove for Recognition of Persian Sign Language, M.Sc. Thesis Sharif University of Technology ; Vossoughi, Gholamreza (Supervisor) ; Parnianpour, Mohammad (Supervisor)
Abstract
Sign language is recognized by considering the combination of the hand gesture, orientation, location and patterns of hand and arm movement. These complex interactions make the character and word recognition very challenging. In this paper, with the aid of sensory gloves and multiple inertial measurement units (IMUs) we measure the shoulder and elbow joint trajectories and hand gesture to train the discriminant functions to recognize the words intended and represented by sign language. For recognition of hand gesture, the Naive Baysian classifier was used while for Eulerian angles a new time series similarity measures were computed. Different aggregation method was used to integrate temporal...
Cataloging briefDesign of Sensory Gove for Recognition of Persian Sign Language, M.Sc. Thesis Sharif University of Technology ; Vossoughi, Gholamreza (Supervisor) ; Parnianpour, Mohammad (Supervisor)
Abstract
Sign language is recognized by considering the combination of the hand gesture, orientation, location and patterns of hand and arm movement. These complex interactions make the character and word recognition very challenging. In this paper, with the aid of sensory gloves and multiple inertial measurement units (IMUs) we measure the shoulder and elbow joint trajectories and hand gesture to train the discriminant functions to recognize the words intended and represented by sign language. For recognition of hand gesture, the Naive Baysian classifier was used while for Eulerian angles a new time series similarity measures were computed. Different aggregation method was used to integrate temporal...
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