Real-time Sign Language Recognition based on Neural Network Architecture

被引：0
|
作者：
Mekala, Priyanka [1 ]
Gao, Ying [2 ]
Fan, Jeffrey [1 ]
Davari, Asad [3 ]
机构：
[1] Florida Int Univ, Dept Elect & Comp Eng, Miami, FL 33199 USA
[2] Univ Wisconsin, Dept Elect Engn, Wismar, Germany
[3] West Virginia Univ Inst Tech, Dept Elect & Comp Engn, Montgomery, WV 25136 USA
来源：
PROCEEDINGS SSST 2011: 43RD IEEE SOUTHEASTERN SYMPOSIUM ON SYSTEM THEORY | 2011年
关键词：
WAVELET NETWORKS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：195 / 199
页数：5
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