## 1 Introduction  
The demand for both quantity and quality of services continues to increase in the field of hand gesture recognition (HGR) [1]. Face-to-Face communication relies heavily on hand gestures [2]. In human-computer interaction systems, hand gesture applications offer a touchless interface [3]. HGR plays an important role in various applications [2], including human-computer interaction (HCI), virtual reality (VR), sign-language interpretation, drone control, and smart home control applications, as shown in Figure 1, highlighting its significant importance in modern technology [4, 5]. Although hand gesture recognition is an active field of computer vision, there are many challenges that exist currently. One of those challenges is the availability of a suitable dataset that can generalize a model during training to perform well in real-world scenarios [6]. Hand gesture classification for practical application scenarios is a challenging task and requires careful design of the dataset and selection of a pre-trained model [7]. Therefore, it is crucial to take into account both the selection of lightweight models for the training and the availability of datasets [8]. By exploiting pre-trained data in the form of transfer learning, the need for a large dataset can be omitted [1]. The important contributions of a quality dataset and a well-trained model are occasionally disregarded, even though researchers frequently concentrate on improving model performance [9]. Due to performance complexity, this omission may result in high hardware needs and impracticability for real-time applications [10]. Our goal is to investigate and select an ideal model and dataset for HGR. Access to a large dataset is critical for the success of any machine learning (ML) method, especially when handling complex problems like hand gesture recognition [11]. In this regard, datasets designed expressly for hand gesture recognition play an important role in furthering research and development in this particular field [12]. These datasets hold both static and dynamic gestures and are very diverse in nature to help generalize models during training [13]. Through careful experiments and testing, researchers can gain insights into the requirements for acquiring data useful for real-world applications like vision-based HCI, drone control, and sign-language interpretation [14; 15], as having diverse hand gesture datasets is essential. Similarly, choosing the best pre-trained deep learning model for hand gesture recognition requires a careful selection procedure [12]. A lot of time is wasted on the experimental evaluation study by the researches in this phase [16]. It also depends on the type of dataset that is going to be used in the transfer learning process [17]. In this study, we focused on identifying the best lightweight model that performs well on the targeted datasets for real-time hand gesture recognition while also exploring which dataset aligns best with each model in terms of performance. Therefore, we analyzed six state-of-the-art lightweight models, such as InceptionV4 [18], NanoViT [19], EfficientNet [20], EdgeNeXt [21], ConvNeXt [22], and MobileNetV3 [23], on four widespread benchmark datasets, each having rich contents and characteristics of hand gestures. This study aims to determine not only the most suitable model but also the dataset-model combinations that maximize performance for real-world scenarios. Through our study, we analyze the overall nature of the datasets [24; 25] and transfer learning models. The factors effecting the quality and nature of the dataset, including the  
Figure 1: Application scenarios of vision-based hand gesture recognition in real-time systems. size of the dataset [26], the diversity of the subjects (also trained to perform gestures) [27], different lighting conditions [28], the quality of the data acquisition sensor and calibration, the resolution of the image, and the overall design and collection of gestures [29], are very important.