## 2 Related Work
### System Level Review  
In the manuscript [2], the authors used a Leap Motion Device (LMD) to acquire hand gesture signals. The D-M can communicate with others using hand gestures while the ND-M are provided with an android application that converts speech to text. The accuracy of gesture detection is 95%. The authors in [3] presented a system that converts hand gestures from the D-M into text. The system also displays a gesture image for the text input. A portable device is designed by the authors in [4]. The D-M can carry this device to acquire and convert hand gestures into speech. In [5], a contactless hand gesture recognition system is presented where the gesture is acquired using an LMD. Long Short-Term Memory (LSTM) recurrent neural networks were used for gesture detection. The authors used an LMD for hand gesture recognition for the D-M in [6]. They defined a criterion for hand gesture recognition and used different methods such as Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and Dynamic Time Warping (DTW) for image data processing. In the manuscript [7], the authors acquired sign language gestures using a camera. Various neural network algorithms were compared to process the image data and calculate the accuracy of the proposed system. The authors in [8] developed a learning system for the D-M people. They used Augmented Reality to convert the acquired gesture into a 3D model. They also developed a glove using Flex sensors. The manuscript [9] is a survey paper where the authors collected and reviewed data to understand different reasons for hearing loss and the age when it is detected. A prototype of a glove using flex sensors is designed by the authors in [10]. They designed a system where hand gestures are acquired and then converted into text and voice. The researchers in [11] reviewed and compared several techniques for hand gesture recognition and listed the novel features of different algorithms, and they also highlighted their limitations. This survey provides a good insight into different algorithms, including their pros and cons. In [12], an automated ASL-based hand gesture recognition system is presented, where hand gestures are first detected and then converted into text. An automated sign language detection system is proposed by the authors in [13], where they designed two-way communication using an ASL dataset. The Bangla Sign Language (BSL) dataset is applied in [14], where the authors processed the hand gesture data using CNN, reporting an accuracy exceeding 98%. In [15], the authors developed a mobile phone application where a customized interface for individual users is proposed. This application provides support for multiple languages. An Arabic Sign language (ArSL) based system is presented by authors in [16]. The detected ArSL hand gestures are converted into voice. In this manuscript, the authors surveyed different hand gesture detection techniques. A CNN-based hand gesture recognition system is presented in [17]. The authors reported a hand gesture recognition accuracy exceeding 93%. In [18], the researchers developed a glove that was used for translating ASL and reported an accuracy of 95%. A portable hand gesture recognition prototype is presented in [19]. The authors of this manuscript used a deep learning algorithm to process image data, which is then converted to speech. In [20], the authors developed a prototype of a smart glove for detecting sign language, and when a gesture is recognized, a recorded audio message is played. This system is designed to detect Indian Sign Language (ISL) data. Another CNN-based hand gesture detection prototype is presented in [21]. The authors claim that the proposed prototype detects ISL with training accuracy exceeding 99% by using more than 3000 images. In [22], the authors used the ASL dataset, where the hand gestures are detected using CNN. They reported an accuracy exceeding 95%. The authors in [23] developed a prototype using a flex sensors-based glove and interfaced it with Arduino.