Study of Convolutional Neural Network in Recognizing Static American Sign Language

被引：0

作者：

Bin, Lee Yi ^{[1
]}

Huann, Goh Yeh ^{[1
]}

Yun, Lum Kin ^{[1
]}

机构：

[1] Tunku Abdul Rahman Univ Coll, Fac Engn & Technol, Dept Mech Engn, Kuala Lumpur, Malaysia

来源：

PROCEEDINGS OF THE 2019 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (IEEE ICSIPA 2019) | 2019年

关键词：

Convolutional Neural Network; American Sign Language; Machine Learning; Computer Vision;

D O I：

10.1109/icsipa45851.2019.8977767

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sign language is a form of communication language to connect a deaf-mute person to the world. It involves the uses of hand gestures and body movement in order to express an idea. Nevertheless, general publics are mostly not educated to comprehend the sign language. For this reason, there is a need to have a translator to facilitate the communication. This paper would like to present a Convolutional Neural Network (CNN) model for predicting American Sign Language. There are 4800 images were captured to train and validate the proposed model. 95% recognition accuracy was attained in experiment, which shows robust performance in recognition 24 static American Sign Language pattern. The successful development of this model can be served as the basis to develop a more complicated sign language translator.

引用

页码：41 / 45

页数：5

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