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首页> 外文会议>IEEE International Conference on Systems, Man, and Cybernetics >HGR: Hand-Gesture-Recognition Based Text Input Method for AR/VR Wearable Devices
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HGR: Hand-Gesture-Recognition Based Text Input Method for AR/VR Wearable Devices

机译：HGR：基于手势识别的AR / VR可穿戴设备的文本输入方法

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摘要

Hand gestures, whether static or dynamic, are a field of intense study and have several potential uses for human-computer interaction in real-time systems. Static and dynamic hand gestures are rudimentary ways for human-computer interaction. This paper presents a technique for the text input method which is hand-gesture-recognition based. This compact hand-based text input system is proposed for augmented reality (AR) and virtual reality (VR) devices. To recognize and classify hand gestures, the hand image is captured by a standard camera. After, the hand is segmented using background subtraction, and then the segmented hand gesture is input in the trained neural network for gesture recognition. Finally, hand movements are tracked and recorded using a convex hull algorithm. The corresponding written character is passed to a trained neural network. The proposed architecture is tested and the experimental results are compared with other methods, which showed that the proposed method performed better than traditional methods and achieved 96.12% accuracy, achieved accuracy is overall better than existing methods.
机译：手势，无论是静态还是动态，都紧张的研究领域，并有实时系统的人机交互几个潜在用途。静态和动态手势是人机交互的方式简陋。本文提出了这是基于手的手势识别的文字输入方法的技术。这种紧凑的基于手的文本输入系统提出了一种用于增强现实（AR）和虚拟现实（VR）的设备。识别和分类手势，手图像由标准照相机捕获。后，将手使用背景减除分割，然后将分割的手势是手势识别训练的神经网络中的输入。最后，手部动作跟踪和使用的凸包算法记录。相应的文字字符传递给训练的神经网络。所提出的架构进行了测试和实验结果与其它方法，这表明，所提出的方法比传统方法更好的执行，取得了96.12％的准确度相比，取得了精度总体优于现有方法。

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