手语到文本和语音的转换以及手势的预测

Srinidhi Madhyastha, R. Girishu, M. Varuna, G. PoornimaB.
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引用次数: 2

摘要

聋哑人依靠手语来表达自己的思想和感情。它成为聋人与其他人沟通的主要障碍。手语已经发展成为计算机视觉研究的主要领域之一。手语识别的研究人员使用不同的输入设备,如数据手套、网络摄像头、深度摄像头、彩色摄像头、微软的Kinect传感器等来捕捉手势。在本文中,我们展示了手语的重要性,并提出了分类技术和有效的结果。手语通过手的交流和肢体语言来传达意思,而不是通过声音来传达声音模式,后者涉及手的形状、方向和动作的同时组合。这些信号是通过一种名为“Leap Motion Controller”的新型数字传感器捕捉到的。LMC是一种3D非接触式运动传感器,可以跟踪和检测手、手指、骨头和手指状物体。Leap设备跟踪由Leap运动控制器生成的点、波、到达、抓取等数据。该系统实现了动态时间扭曲(DTW),用于将手势转换为适当的文本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conversion of Sign Language to Text and Speech and Prediction of Gesture
Deaf people rely on sign language to express their own thoughts and feelings. It becomes the major communication barrier between the deaf and other people. Sign Language has evolved as one of the major areas of research and study in computer vision. Researchers in sign language recognition used different input devices such as data gloves, web camera, depth camera, color camera, Microsoft's Kinect sensor, etc. to capture hand signs. In this paper, we display the importance of Sign Language and proposed technique for classification and their efficient results. A sign language looks up the manual communication and body language to convey meaning, as opposed to acoustically conveyed sound patterns, which involve a simultaneous combination of hand shapes, orientation, and movement of hands. The signs are captured using a new digital sensor called “Leap Motion Controller”. LMC is 3D non-contact motion sensor which can track and detects hands, fingers, bones and finger-like objects. The Leap device tracks the data like point, wave, reach, grab which is generated by a leap motion controller. The system implements Dynamic Time Warping (DTW) for converting the hand gestures into an appropriate text.
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