Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network

被引：15

作者：

Gangrade, Jayesh ^{[1
,2
]}

Bharti, Jyoti ^{[3
]}

机构：

[1] IES Indore, Maunala Azad Natl Inst Technol Bhopal, Indore, India

[2] IES Indore, IPS Acad, Indore, India

[3] Maunala Azad Natl Inst Technol, Dept Comp Sci & Engn, Bhopal, India

来源：

IETE JOURNAL OF RESEARCH | 2023年 / 69卷 / 02期

关键词：

Convolution neural network (CNN); Hard of hearing (HoH); Indian sign language (ISL); Microsoft Kinect sensor;

D O I：

10.1080/03772063.2020.1838342

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：

Hearing-impaired people can interact with other people through sign language. The proposed system tears down the communication barrier between Hard of hearing (HoH) community and those who do not know their sign language. In this paper, we have developed an algorithm to detect and segment the hand region from a depth image using the Microsoft Kinect sensor. The proposed algorithm works well in the cluttered environment, e.g. skin color background and hand overlaps the face. Convolution Neural networks (CNN) are applied to automatically construct features from Indian sign language (ISL) signs. These features are invariant to rotation and scaling. The proposed system recognizes gestures accurately up to 99.3%.

引用

页码：723 / 732

页数：10

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