DocumentCode
2995161
Title
Real-time sign language recognition based on neural network architecture
Author
Mekala, Priyanka ; Gao, Ying ; Fan, Jeffrey ; Davari, Asad
Author_Institution
Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
fYear
2011
fDate
14-16 March 2011
Firstpage
195
Lastpage
199
Abstract
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.
Keywords
gesture recognition; language translation; neural nets; autonomous translator; neural network architecture; point of interest; real-time sign language recognition; storage memory requirement; streaming images; text format; track point; voice format; Artificial neural networks; Cameras; Computer architecture; Feature extraction; Handicapped aids; Noise; Real time systems;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory (SSST), 2011 IEEE 43rd Southeastern Symposium on
Conference_Location
Auburn, AL
ISSN
0094-2898
Print_ISBN
978-1-4244-9594-8
Type
conf
DOI
10.1109/SSST.2011.5753805
Filename
5753805
Link To Document