## 2 Literature Review
The researchers also added a vocabulary of 100 words. The application also carries out analysis and assessment, which helps the user. In [23], a mobile phone application 'BridgeApp' is developed for DnM people. They used speech-to-text, speech-to-visual, and sign language features in their implementation. This application works in offline mode. They have implemented American Sign Language (ASL) and Filipino Sign Language (FSL). A system based on vibrotactile, and visual feedback is developed in [24]. DnM people can communicate on their own without the use of sign language using this system. An Android application is also developed by the authors. The authors in [25] used ARM LPC-2148 to connect different sensors and actuators to a Braille keypad, which is a user-friendly application for blind people. They have also installed sensors in walking sticks to aid people who are visually impaired. A Hand Gesture Recognition System for DnM people is designed using a Fuzzy-Neural Network [26]. The data was acquired from a hand gesture translating glove. The accuracy of gesture recognition using fuzzy logic is discussed. In [27], an application is developed for DnM, which detects facial emotion. The application performs eye-tracking and event-related potential analysis. The authors carried out extensive testing using 630 images of hand gestures. A deep learning-based hand gesture translation system is presented in [28]. The software automatically recognized hand gestures at 94% accuracy. A process to convert to and from sign language is discussed in [29]. The gestures are detected and then converted into text. To develop an effective model, a variety of ML and AI techniques are used, in addition to natural language processing (NLP) and convolutional neural networks CNN). The authors also used attention-based long short-term memory (LSTM) to detect rapid and continuous lip movements. An intelligent glove for sign language communication is discussed in [30]. The researchers used a flex sensor and a GY-521 module interfaced with Arduino. The flex sensor measures the movement of the fingers and generates a voltage output. The acquired hand gesture data are then converted into text. In [31], the authors developed a module that translated sign language. The user can wear it around the neck. The device processes hand gesture images using image processing techniques and deep learning models to recognize the sign, which is then converted into a voice using a text-to-speech convertor. A CNN-based system is presented in [32], which also provides a training option. This system also supports multiple languages. In [33], a low-cost Arduino-based system connected to flex sensors is presented. This system is used for gesture detection. The system also uses a gyroscope and an accelerometer. A picture-based communication application is presented in [34]. It is a portable, easy-to-use communication tool that helps people communicate with each other. A Chinese language translation and processing tool is presented in [35]. The DnM people can watch videos with Chinese subtitles using this tool. In [36], a video chat application is developed. This is based on the Indian Sign language (ISL). The process is initiated when a user starts making hand gestures, which are then picked up by a camera. The algorithm, which is based on CNN, then converts this into phrases and numbers. A medical consultation system is presented in [37], where the DnM person can attend a hospital and communicate with the doctor. The system provides two-way communication. In [38; 39], the authors applied machine learning to solve a new problem for setting up electronic product test sites. They created a dataset for the machine learning algorithm. The creation of a sign language dataset is outside the scope of this work. The following references provide the details of some available sign language datasets. In [40], the authors presented the Argentinian sign language dataset. They included 3200 videos in the dataset.