Machine Learning Techniques for Indian Sign Language Recognition

被引：0

作者：

Dutta, Kusumika Krori ^{[1
]}

Bellary, Sunny Arokia Swamy ^{[2
]}

机构：

[1] MS Ramaiah Inst Technol, Dept Elect & Elect Engn, Bengaluru 560054, India

[2] Univ North Carolina Charlotte, Dept Elect & Comp Engn, USA, NC 28223 USA

来源：

2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC) | 2017年

关键词：

Indian Sign Language; Deaf and Mute; Machine learning; Double handed sign language; single handed sign language; Dimensionality reduction; ANN; MATLAB; FACIAL EXPRESSION;

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：

Over the years, communication has played a vital role in exchange of information and feelings in one's life. Sign language is the only medium through which specially abled people can connect to rest of the world through different hand gestures. With the advances in machine learning techniques, Hand gesture recognition (HGR) became a very important research topic. This paper deals with the classification of single and double handed Indian sign language recognition using machine learning algorithm with the help of MATLAB with 92-100% of accuracy.

引用

页码：333 / 336

页数：4

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