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
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;
Conference_Titel :
System Theory (SSST), 2011 IEEE 43rd Southeastern Symposium on
Conference_Location :
Auburn, AL
Print_ISBN :
978-1-4244-9594-8
DOI :
10.1109/SSST.2011.5753805
