文章基本信息

标题：Arabic and American Sign Languages Alphabet Recognition by Convolutional Neural Network
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作者：Shroog Alshomrani ; Shroog Alshomrani ; Lina Aljoudi 等
期刊名称：Advances in Science and Technology Research Journal
印刷版ISSN：2080-4075
电子版ISSN：2299-8624
出版年度：2021
卷号：15
期号：4
页码：136-148
DOI：10.12913/22998624/142012
语种：English
出版社：Society of Polish Mechanical Engineers and Technicians
摘要：Hearing loss is a common disability that occurs in many people worldwide. Hearing loss can be mild to complete deafness. Sign language is used to communicate with the deaf community. Sign language comprises hand gestures and facial expressions. However, people fi nd it challenging to communicate in sign language as not all know sign language. Every country has developed its sign language like spoken languages, and there is no standard syntax and grammatical structure. The main objective of this research is to facilitate the communication between deaf people and the community around them. Since sign language contains gestures for words, sentences, and letters, this research implemented a system to automatically recognize the gestures and signs using imaging devices like cameras. Two types of sign languages are considered, namely, American sign language and Arabic sign language. We have used the convolutional neural network (CNN) to classify the images into signs. Diff erent settings of CNN are tried for Arabic and American sign datasets. CNN-2 consisting of two hidden layers produced the best results (accuracy of 96.4%) for the Arabic sign language dataset. CNN-3, composed of three hidden layers, achieved an accuracy of 99.6% for the American sign dataset.
关键词：convolutional neural network;deep learning;American sign language;Arabic sign language