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Real-time sign language recognition based on neural network architecture

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4 Author(s)
Mekala, P. ; Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA ; Gao, Y. ; Fan, J. ; Davari, A.

In real-time, it is highly essential to have an autonomous translator that can process the images and recognize the signs very fast at the speed of streaming images. In this paper, architecture is being proposed using the neural networks identification and tracking to translate the sign language to a voice/text format. Introduction of Point of Interest (POI) and track point provides novelty and reduces the storage memory requirement.

Published in:

System Theory (SSST), 2011 IEEE 43rd Southeastern Symposium on

Date of Conference:

14-16 March 2011