## 2 Related Works
### Image Acquisition Technologies  
Image acquisition is the most critical part of any gesture recognition system, because it requires to find a trade-off between high data quality and overall system performance. In recent years, the ever evolving 3D acquisition technologies made it possible to obtain RGB and Depth information at the same time, in light of providing more robust and consistent solutions [24]. In 2D technologies, a single RGB camera is used for capturing two-dimensional images of the hands or body. This approach is widely adopted due to its cost-effectiveness and availability, since these kind of cameras are the most common ones to the general users. However, it presents some drawbacks, such as sensitivity to lighting conditions and difficulties in distinguishing between similar movements on different planes. Despite these limitations, 2D technologies can be quite effective when supported by robust image processing algorithms. For instance, [PERSON] et al. [25] demonstrated the effectiveness of using MediaPipe and deep learning in real-time sign language recognition, showing that the 2D approach can effectively manage complex gestures.  
Conversely, 3D technologies rely on depth sensors to capture three-dimensional information about the movement and position of hands and body. In contrast to 2D systems, these provide higher accurateness and robustness in the capturing process, since they can detect the change in depth and movement in space independently from lighting conditions. For example, [PERSON] et al. [20] proposed a methodology using Kinect that may substantially increase the preciseness of gesture recognition, thereby making it especially effective for complex applications in the process of sign language teaching. One of the most important advantages of 3D technologies is their ability to capture complex and detailed movements, such as rotations and translations of the hands, demanding tracing using 2D technologies. This hardware is, as a rule, more expensive and less convenient for everyday use than 2D. In addition, [PERSON] et al. [21] developed Kinect technology for the recognition of the whole body, expanding the functionality of sign language teaching with the help of recognizing gestures that would activate other parts of the body in communication, not just hand movements. Nowadays, 3D information is not only used to increase hand gesture algorithms recognition rate, but also to distinguish sign language linguistic from gestural expressions and increase the overall robustness, as proved by [PERSON] et al. [26].  
[PERSON] et al. [25] further compared the two technologies and noted several key differences. They found that while 2D systems are accessible and practical for many applications due to their lower cost and ease of use, they suffer from limitations in accuracy under varying lighting conditions and can struggle with depth perception. On the other hand, 3D systems, such as those utilizing Kinect, offer superior accuracy in detecting complex gestures and depth information, but at a higher cost and with greater hardware requirements. The authors concluded that the choice between 2D and 3D technologies should be based on the specific needs and resources of the application and in some cases, a combination of both may provide the optimal solution for accurate and reliable gesture recognition. Finally, both the technologies, 2D and 3D, come with their set of advantages and disadvantages. Their application would depend on the need and available resources.