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Sign Language Interpreter System: An alternative system for machine learning

机译：手语翻译系统：机器学习的替代系统

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Losing the ability to speak exerts psychological and social impacts on the affected people due to the lack of proper communication. Thus, Sign Language (SL) is considered a boon to people with hearing and speech impairment. SL has developed as a handy mean of communication that form the core of local deaf cultures. It is a visual–spatial language based on positional and visual components, such as the shape of fingers and hands, their location and orientation as well as arm and body movements. The problem is that SL is not understood by everyone, forming a communication gap between the mute and the able people. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand to bridge this communication gap, as the proposed system. The proposed model is a glove equipped with five flex sensors, interfacing with a control unit fixed on the arm, translating American Sign Language (ASL) and Arabic Sign Language (ArSL) to both text and speech, displayed on a simple Graphical User Interface (GUI). The proposed system aims to provide an affordable and user friendly SL translator system, working on the basis of Machine Learning (ML). However, it adapts to each person’s hand instead of using a generic data set. The system achieved 95% recognition rate with static gestures and up to 88% with dynamic gestures.
机译：由于缺乏适当的沟通，丧失说话能力会对受影响的人们产生心理和社会影响。因此，手语（SL）被认为对听觉和语言障碍的人有好处。 SL已经发展成为一种方便的沟通手段，它构成了当地聋人文化的核心。它是一种视觉空间语言，基于位置和视觉组件，例如手指和手的形状，它们的位置和方向以及手臂和身体的移动。问题在于，每个人都无法理解SL，从而在哑巴和有才能的人之间形成了沟通鸿沟。为了克服与残疾相关的困难，已经实施了多种和系统的学术干预措施，这些干预措施根据具体情况而有所不同。基于感官手套的手语识别（SLR）系统是一项重大的创新，旨在获取有关人手的形状或动作的数据以弥合这种交流差距，这是拟议的系统。拟议的模型是一种配备有五个挠性传感器的手套，该手套与固定在手臂上的控制单元接口，将美国手语（ASL）和阿拉伯手语（ArSL）转换为文本和语音，并显示在简单的图形用户界面（ GUI）。拟议的系统旨在提供一种基于机器学习（ML）的价格合理且用户友好的SL转换器系统。但是，它可以适应每个人的手，而无需使用通用数据集。该系统在静态手势下的识别率达到95％，在动态手势下的识别率高达88％。

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