## 2 Related Work  
Hand gesture recognition has been extensively used for real-world application scenarios, like drone control, sign-language interpretation, computer vision-based HCI, and more [7; 13]. Several studies have reported the insights gained related to datasets, models, and performance improvement. Due to the computational complexity of convolutional neural networks (CNNs), the focus has shifted to lightweight models to achieve performance accuracy [10]. Similarly, with the popularity of these data-hungry models, the focus is always on the creation of a large dataset targeting quantity alone while ignoring the quality. Several factors contribute to the quality of a dataset, i.e., biases, lighting conditions, the type of sensor used, the distances and locations of objects in the frames, the number of subjects involved, etc. So, while studies have shown deep interest in improving the accuracy of the models, fewer efforts have been made to analyze the nature and diversity of datasets, which affects the overall performance of a model in training.  
[PERSON] et al. [34] evaluated several HGR techniques for drone control. They focused on the model's accuracy in real time. While they reported performance comparison results of several models, their study lacked an evaluation of benchmark datasets. [PERSON] et al. [35] investigated CNN-based models for real-world scenarios. Despite their high accuracy, due to their high computational complexities, these models were unable to be used in real-time scenarios for embedded systems. Unlike lightweight models, CNNs are the least suitable for diverse scenarios and real-world applications. Another notable work reported by [PERSON] et al. [36] investigated a lightweight model for HGR. While the approach mostly focused on model size reduction, the study lacked an exploration of a benchmark dataset for improving the applicability in a real-world allocation scenario.  
Last but not the least, [PERSON] and [PERSON] et al. [37] investigated HGR methods, including sensor-based and vision-based approaches. Although they briefly provided the trade-off between accuracy, lightweight models, and applicability in real-world scenarios, the study lacked a comparison of transfer learning models against benchmark datasets for real-time applications, like drone control and vision-based HCI systems.  
Hence, most studies have often overlooked the crucial aspect of dataset analysis and the use of a suitable transfer learning model. A recent advancement in hand gesture recognition has been significant due to the availability of diverse datasets and ML techniques, but challenges still exist in robust and precise gesture classification, especially in real-time interaction-based systems (i.e., drone control, sign-language processing, etc.). Several studies have utilized various datasets to study and assess hand gesture recognition, for example, the Egogesture [38], NVGesture [39], Jester [40], and SignLanguage [41] datasets have been extensively used in the literature, each reporting reasonable accuracy by their respective authors.  
To address the gap discussed above, we discuss our detailed pipeline in the following sections.