## 2 Related Work
### System Level Review
The prototype converts the hand gesture data into text. The authors used K-Nearest Neighbours (KNN) algorithm for image data processing. A PC-based image detection system is presented in [24]. The authors used the Principal Component Analysis (PCA) algorithm. In [25], an automated Sign Language Interpreter (SLI) prototype, which is based on a custom-made glove connected to Arduino, is presented. The authors reported a hand gesture detection accuracy of 93%. A CNN-based prototype for hand gesture detection is presented in [26]. This prototype also provides a training option and support for multiple languages. The authors developed a mobile application in [27] for the D-M. The application works in offline mode and uses ASL and Filipino Sign Language (FPL) datasets. A PC-based prototype is presented by authors in [28] for detecting hand images using sensors. An android-based application is presented in [29], where visual feedback is provided to the user. The authors in this paper also compared different technologies. In [30], the authors carried out a survey to understand the problems and challenges faced by the D-M. They highlighted the importance and impact of hand gesture recognition systems and prototypes in providing a platform for the D-M.  
Table 1 is a summary of the undertaken review. The details include hardware, software, features, and limitations of the designed prototypes and products. The last column also includes information related to Sign Language Dataset (SLD). \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Hardware/Software** & **Summary** \\ \hline
[2] & LMD, Android app & D-M to ND-M to D-M, Single SLD \\
[3] & PC, Harris algorithm & D-M to ND-M to D-M, Single SLD \\
[4] & Raspberry Pi & D-M to ND-M, Single SLD \\
[5] & LMD, LSTM Neural networks & D-M to ND-M, Single SLD \\
[6] & LMD, (GMM, HMM, DTW) & D-M to ND-M, Single SLD \\
[7] & PC, NN algorithms & D-M to ND-M, Single SLD \\
[8] & PC, Arduino, Gloves, Flex sensors & Calibration required for the custom-made product, D-M to ND-M, Single SLD \\
[10] & Arduino, Gloves, Sensors & Calibration required for the custom-made product, D-M to ND-M, Single SLD \\
[12] & PC, TensorFlow & D-M to ND-M, Single SLD \\
[13] & PC, Various ML algorithms & D-M to ND-M to D-M, Single SLD \\
[14] & PC, TensorFlow, CNN & D-M to ND-M, Single SLD \\
[15] & Android & D-M to ND-M, Multi SLD \\
[16] & Android & D-M to ND-M, Single SLD \\
[17] & PC, CNN & D-M to ND-M, Single SLD \\
[18] & Arduino, Gloves, Flex sensors & Calibration required for the custom-made product, D-M to ND-M, Single SLD \\
[19] & PC NN & D-M to ND-M, Single SLD \\
[20] & Gloves, Flex sensors, Voice & D-M to ND-M, Single SLD \\
[21] & recorder, Matlab & D-M to ND-M, Single SLD \\
[22] & PC, CNN & D-M to ND-M, Single SLD \\
[23] & Gloves, Sensors, Arduino, KNN & D-M to ND-M, Single SLD \\
[24] & PC-based, PCA & D-M to ND-M, Single SLD \\
[25] & Arduino, Gloves, Sensors, KNN & Calibration required for the custom-made product, D-M to ND-M, Single SLD \\
[26] & PC-based, CNN & D-M to ND-M, Single SLD \\
[27] & Android application & D-M to ND-M, Multi SLD \\
[28] & PC, CNN & D-M to ND-M, Single SLD \\
[29] & Android & D-M to ND-M to D-M, Single SLD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work review.