According to the World Health Organization (WHO), 466 million people are suffering from hearing loss, i.e., 5% of the world population, of which 432 million (93%) are adults and 34 million (17%) children. The main problem is how deaf and hearing-impaired communicate with people and each other, how they get education or do their daily activities. Sign language is the main communication method for them. Building automatic hand gestures recognition system has many challenges specially in Arabic. Solving recognition problem and practically develop real-time recognition system is another challenge. Several types of research have been conducted on sign language recognition systems but for Arabic Sign Language are very limited. In this paper, an Arabic Sign Language (ArSL) recognition system that uses a Leap Motion Controller and Latte Panda is introduced. The recognition phase depends on two machine learning algorithms: (a) KNN (k-Nearest Neighbor) and (b) SVM (Support Vector Machine). Afterwards, an Ada-Boosting technique is applied to enhance the accuracy of both algorithms. A direct matching technique, DTW (Dynamic Time Wrapping), is applied and compared with AdaBoost. The proposed system is applied on 30 hand gestures which are composed of 20 single-hand gestures and 10 double-hand gestures. The experimental results show that the DTW achieved an accuracy of 88% for single-hand gestures and 86% for double-hand gestures. Overall, the proposed model’s recognition rate reached 92.3% for single-hand gestures and 93% for double-hand gestures after applying the Ada-Boosting. Finally, a prototype of our model was implemented in a single board (Latte Panda) to increase the system’s reliability and mobility.

根据世界卫生组织（WHO）的数据，有4.66亿人患有听力障碍，即占世界人口的5％，其中4.32亿（93％）是成年人和3400万（17％）儿童。主要问题是耳聋和听力障碍者与人和人之间如何交流，他们如何获得教育或进行日常活动。手语是他们的主要交流方式。建立自动手势识别系统存在许多挑战，特别是阿拉伯语。解决识别问题并切实开发实时识别系统是另一个挑战。关于手语识别系统已经进行了几种类型的研究，但是对于阿拉伯手语却非常有限。在本文中，介绍了使用Leap Motion控制器和Latte Panda的阿拉伯手语（ArSL）识别系统。识别阶段取决于两种机器学习算法：（a）KNN（k最近邻）和（b）SVM（支持向量机）。之后，采用Ada-Boosting技术来提高两种算法的准确性。应用了直接匹配技术DTW（动态时间包装）并将其与AdaBoost进行比较。所提出的系统应用于30个手势，这些手势由20个单手手势和10个双手手势组成。实验结果表明，DTW的单手手势准确率达到88％，双手手势准确率达到86％。总体而言，在应用了Ada-Boosting后，该模型的单手手势识别率达到92.3％，双手手势识别率达到93％。最后，