# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 1 Introduction  
Hand gestures are powerful tools for communication, and they are often used in conjunction with speech to enhance the expression of intentions and convey information. They also serve as a standalone method of communication, particularly in sign language, especially for individuals who are mute or deaf. This research paper primarily focuses on American Sign Language (ASL), which is recognized as the most commonly utilized sign language. Despite the general awareness of sign language, few people understand it and can effectively communicate using it. To address this issue, a hand gesture recognition (HGR) system is needed. HGR models hold significant promise for a wide range of applications. In interactive computing interfaces, HGR can enable intuitive and hands-free control, finding applications in gaming, virtual reality (VR), and augmented reality (AR) systems. In the healthcare domain, HGR can be utilized for developing assistive technologies, allowing individuals with mobility impairments to control devices through hand gestures. HGR also plays a crucial role in human-robot interactions, where robots can interpret and respond to human gestures, enhancing collaboration and communication in various settings. Moreover, HGR has potential applications in sign language interpretation, contributing to improved communication accessibility for individuals with hearing impairments. The technology can also be applied in security and surveillance, allowing for gesture-based commands formonitoring and controlling security systems. As HGR technology advances, its applications are expected to expand, offering innovative solutions in diverse fields. HGR can be broadly categorized into two types: static HGR [1; 2; 3; 4; 5; 6; 7; 8] and dynamic HGR [9; 10; 11; 12; 13]. The former involves classifying hand gestures from images, while the latter involves classifying hand gestures from videos. Dynamic hand gesture recognition is more complex than static HGR due to the numerous possibilities and combinations of hand gestures that can occur in a video. Static HGR can be further divided into two main types. The first type relies on vision, where images are captured using a camera. The second type involves wearable devices such as data gloves, which can capture hand gesture information more accurately but are inconvenient and expensive. Identifying hand gestures in both scenarios poses numerous obstacles, such as fluctuations in lighting circumstances, intricate backgrounds, resemblances between different hand gestures, and differences in hand dimensions and skin complexion. This research will specifically focus on vision-based static HGR. Vision-based static HGR can be categorized into two main approaches: hand-crafted and deep learning. The hand-crafted approach involves applying various feature extraction techniques, such as discrete wavelet transform (DWT) [14; 15; 16; 17], histogram of oriented gradient (HOG) [18; 19; 20], and scale invariant feature transform (SIFT) [21], to extract features from the hand gestures. These extracted characteristics are subsequently input into a classification algorithm for the purpose of gesture categorization. Alternatively, the deep learning approach utilizes deep learning networks, such as convolutional neural networks (CNNs) [1; 2; 3; 4; 5; 6; 22; 23; 24; 25; 26; 27; 28; 29], artificial neural networks (ANNs) [30], and autoencoders [31; 32], to automatically extract features from the hand gestures. Deep learning networks showcase adaptability to various challenges in static hand gesture recognition, learning from extensive datasets and accommodating diverse environmental factors such as lighting conditions, complex backgrounds, and variations in hand size and skin tone. However, existing methods encounter several limitations that present research gaps to be addressed. One common challenge is the limited capacity of current models to effectively capture global dependencies and intricate relationships among different parts of static hand gestures.