Air-writing is an attractive method of interaction between human and machine due to lack of any interface device on the user side. After removing existing limitations and solving the current challenges, it can be used in many applications in the future. In this paper, using the Kinect depth and color images, an air-writing system is proposed to identify single characters such as digits or letters and connected characters such as numbers or words. In this system, automatic clustering, slope variations detection, and a novel analytical classification are proposed as new approaches to eliminate noise in the trajectory from the depth image and hand segmentation, to extract the feature vector, and to identify the character from the feature vector, respectively. Experimental results show that the proposed system can successfully identify single characters and connected characters with the average recognition rate of 97%. It provides a better result than other similar approaches proposed in the literature. In the proposed system, the character recognition time is quite low, about 3 ms, because of using a novel analytical classifier. Evaluation of 4 classifiers shows that the proposed classifier has a higher speed and precision than the SVM, HMM, and K-nearest neighbors classifiers.

中文翻译：

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带有新型分析分类器的基于Kinect的实时空中书写系统

由于用户端缺少任何接口设备，空中书写是人机交互的一种有吸引力的方法。在消除现有限制并解决当前挑战之后，它可以在未来的许多应用中使用。在本文中，利用Kinect深度和彩色图像，提出了一种空气书写系统，用于识别单个字符（例如数字或字母）和连接的字符（例如数字或单词）。在该系统中，提出了自动聚类，坡度变化检测和新颖的分析分类作为消除深度图像和手分割中轨迹噪声，提取特征向量并从特征向量识别特征的新方法。 ， 分别。实验结果表明，该系统能够成功识别单个字符和关联字符，平均识别率达到97％。与文献中提出的其他类似方法相比，它提供了更好的结果。在提出的系统中，由于使用了新颖的分析分类器，因此字符识别时间非常短，约为3 ms。对4个分类器的评估表明，与SVM，HMM和K最近邻分类器相比，所提出的分类器具有更高的速度和精度。