Hand Gesture Recognition for Filipino Sign Language Under Different Backgrounds

Mark Christian Ang, Karl Richmond C. Taguibao, C. O. Manlises
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引用次数: 4

Abstract

The article implements a hand gesture Filipino Sign Language recognition model using Raspberry Pi. Numerous studies on Filipino Sign Language (FSL) frequently identify a letter with a glove and using a plain background, which may be challenging if implemented in a more complex background. Limited research on the implementation of YOLO-Lite and MobileNetV2 on FSL were also observed. Implementing YOLO-Lite for hand detection and MobileNetV2 for classification, the average accuracy achieved for differentiating 26 hand gestures, representing FSL letters, was 93.29%. The model demonstrated dependability in a variety of complex backgrounds. However, challenges in recognizing letters Q, J, and Z were encountered. Additionally, in letters N and M, due to their similar hand structures, N is sometimes mistakenly interpreted as M. The model developed by the researchers performed well and demonstrated better accuracy compared to a different model. The system was able to achieve higher accuracy while running on limited resources and in various environments.
不同背景下菲律宾手语的手势识别
本文利用树莓派实现了一个手势菲律宾手语识别模型。许多关于菲律宾手语(FSL)的研究经常用手套和普通背景识别一个字母,如果在更复杂的背景中实施,这可能是具有挑战性的。还观察到在FSL上实现YOLO-Lite和MobileNetV2的有限研究。使用YOLO-Lite进行手部检测,使用MobileNetV2进行分类,对26种代表FSL字母的手势进行识别的平均准确率为93.29%。该模型在各种复杂背景下显示了可靠性。然而,在识别字母Q, J和Z方面遇到了挑战。此外,在字母N和M中,由于它们相似的手部结构,N有时会被错误地解释为M。与其他模型相比,研究人员开发的模型表现良好,并且显示出更好的准确性。该系统能够在有限资源和各种环境下实现更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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