未经授权:人类使用书面语或肢体语言(动作)等语言系统相互交流,手部动作,头部手势,面部表情,嘴唇运动,还有更多。理解手语与学习自然语言一样重要。手语是聋哑障碍或残障人士的主要交流方式。没有翻译,有听觉障碍的人与其他人说话有困难。利用机器学习技术自动识别手语识别的研究最近显示出非凡的成功并取得了重大进展。这项研究的主要目的是对迄今为止通过机器学习分类器识别乌尔都语手语的所有工作进行文献综述。
未经评估:所有的研究都是从数据库中提取的,即,PubMed,IEEE,科学直接,和谷歌学者,使用一组结构化的关键字。每一项研究都经过了适当的筛选标准,即,排除和纳入标准。在整个文献综述中,PRISMA指南得到了充分遵循和实施。
UNASSIGNED:本文献综述包括20篇符合资格要求的研究文章。只有这些文章被选择用于额外的全文筛选,符合同行评审和研究文章的资格要求,以及在可信期刊和会议记录中发表的研究,直到2021年7月。经过其他筛选,仅包括基于乌尔都语手语的研究。该筛选的结果分为两部分;(1)乌尔都语手语可用的所有数据集的摘要。(2)总结了所有用于识别乌尔都语手语的机器学习技术。
UNASSIGNED:我们的研究发现,只有一个公开可用的基于USL符号的数据集,其中包含图片与许多字符-,number-,或基于句子的公开数据集。还得出结论,除了支持向量机和神经网络之外,唯一分类器不会被多次使用。此外,没有研究人员选择无监督的机器学习分类器进行检测。据我们所知,这是对应用于乌尔都语手语的机器学习方法进行的第一篇文献综述。
UNASSIGNED: Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature
review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date.
UNASSIGNED: All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature
review.
UNASSIGNED: This literature
review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language.
UNASSIGNED: Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature
review conducted on machine learning approaches applied to Urdu sign language.