Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

• 作者: Farid Al-Zboun Manar Maraqa Mufleh Dhyabat Raed Abu Zitar
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 工学
• 关键词: ARABIC Sign Language FEEDFORWARD NEURAL NETWORKS Recurrent NEURAL NETWORKS GESTURE RECOGNITION
• 年，卷（期）: 2012,（1）
• 页码范围: 41-52
• 页数: 12页
• 分类号: TP39

摘要

The objective of this research is to introduce the use of different types of neural networks in human hand gesture recognition for static images as well as for dynamic gestures. This work focuses on the ability of neural networks to assist in Arabic Sign Language (ArSL) hand gesture recognition. We have presented the use of feedforward neural networks and recurrent neural networks along with its different architectures;partially and fully recurrent networks. Then we have tested our proposed system;the results of the experiment have showed that the suggested system with the fully recurrent architecture has had a performance with an accuracy rate 95% for static gesture recognition.