基于计算机视觉的印尼语手语识别的手骨架图特征

Edy Maryadi, S. Syahrul, Dea Maulidya, R. Risnandar, E. Prakasa, Dian Andriana
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引用次数: 0

摘要

手语是聋人交流的一种方式。印度尼西亚手语或BISINDO是印度尼西亚使用的手语之一。对于聋哑人来说,手语是一种有效的交流手段,但对于有听力的聋哑人来说却不是。这部分是由于听力正常的人对如何与聋人交流的基本知识不足。需要一个手语翻译来帮助聋哑人与听力正常的人交流。手语译者的局限性是本研究开发手语识别方法的原因。本研究是关于基于计算机视觉的基本手语字母和数字识别方法的开发。基本的手语字母和数字是用手臂来展示的,因此可以作为识别字母和数字的基础。本研究提取骨架图。特征从角度作为每个选定顶点的方向获得。这些特征被称为基于骨架的。为了计算基于特征的字母和数字的相似性,本研究使用了k -最近邻(KNN)。对手语字母的识别准确率为99.70%,对手语数字的识别准确率为99.81%。
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Hand Skeleton Graph Feature for Indonesian Sign Language (BISINDO) Recognition Based on Computer Vision
Sign language is a means of communication for The Deaf. Indonesian Sign language or BISINDO is one of the sign languages that is used in Indonesia. For The Deaf with The Deaf sign language is a means of communicating effectively, but not for The Deaf with the hearing. This is partially due to insufficient basic knowledge of The Hearing about how to communicate with The Deaf. A sign language translator needed to help The Deaf communicate with The Hearing. Limited of sign language translator is the reason for this research to develop sign language recognition methods. This research is about the development of methods for recognizing basic sign language alphabet and numbers based on computer vision. Basic sign language alphabet and numbers are demonstrated by arms, so they can be the basis to recognize alphabet and number from them. In this research skeletons graphs are extracted. Features are obtained from angle as direction for each chosen vertex. These features are known as skeletal based. To calculate similarity of the alphabet and numbers based on features, this research uses K-Nearest Neighbor (KNN). The best result of recognize sign language alphabet is 99.70% and to recognize sign language numbers the accuracy is 99.81%.
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