论文标题
基于视觉的美国手语分类方法通过深度学习
Vision-Based American Sign Language Classification Approach via Deep Learning
论文作者
论文摘要
听力障碍是部分或全部听力损失的残疾，这导致与社会中其他人的沟通造成重大问题。美国手语（ASL）是听力受损社区使用的最常用语言的标志语言之一。在本文中，我们提出了一个简单的深度学习模型，该模型旨在将美国手语字母分类为消除与残疾有关的沟通障碍的途径的一步。
Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.
