## 2 Related Works  
Researchers have devised various methods in sign language recognition, including physical methods such as the use of wearable devices [7]. In video-based sign language recognition, the two primary approaches are manual feature extraction and deep learning-based approaches. Manual feature extraction involves picking out and analyzing important features from video footage. Techniques like scale-invariant feature transform (SIFT) [8], histogram of oriented gradients (HOG), and optical flow are used to identify these key features. Once identified, machine learning algorithms like support vector machines (SVM), decision trees, or basic neural networks classify these features. The deep learning-based approach uses advanced deep learning models, CNNs, and recurrent neural networks (RNNs) to identify and learn features from video data automatically. The models feed on the raw video and autonomously carry out feature extraction and classification. There are two subtypes in this method, continuous sign language recognition (CSLR) and isolated sign language recognition (ISLR). CSLR aims to recognize the flowing sign language from videos. It tackles the challenge of identifying and interpreting sign language in a continuous stream, where signs flow into each other. In CSLR, the study by [PERSON] et al. [9] focused on the correlation between frames. In contrast, ISLR focuses on recognizing individual signs or short phrases that are shown separately. The most common approach in ISLR involves CNN-based models like I3D [10] and R3D [11], which classify sign language using RGB video as input. This is simpler than CSLR, as it does not involve interpreting transitions between signs. Researchers widely use CNN models, sometimes combined with RNNs, in ISLR. [PERSON] et al. [12] also recently proposed an ISLR approach using the ViT classifying hand gesture images as input data. Their results showed accuracies of 99.98% on the American Sign Language (ASL) dataset, 99.36% on the ASL with the Digits dataset, and 99.85% on the National University of Singapore (NUS) hand gesture dataset. [PERSON] et al. 's study [13] explored four transformer-based approaches and four pre-training data regimes, investigating all combinations on the WLASL2000 dataset. The model known as MaskFeat achieved 79.02% accuracy in gloss-based WLASL2000, outperforming pose-based and supervised video models. OpenPose [14] and MediaPipe have led to the development of skeleton-based ISLR. A study by [PERSON] [3] proposed the skeleton-aware multimodal framework (SAM-SLR-v2), achieving 98% accuracy test the AUTSLdataset. Improving hand pose estimation can significantly enhance the accuracy of sign language recognition. Recent developments have witnessed emerging hybrid methodologies, amalgamating elements from both manual feature extraction and deep learning paradigms, endeavoring to harness the synergistic strengths of both approaches. Technological advancements in vision transformers (ViTs), OpenPose, and MediaPipe are progressively augmenting the efficacy of these approaches, particularly within the deep learning spectrum. Based on these findings, this study proposes a sign language recognition method using ViT and MediaPipe. While there is research on ASL and other foreign sign languages, studies on Japanese sign language are less advanced in comparison. In their study, [PERSON] et al. [15] used OpenPose and MediaPipe to estimate posture and hand shape, and they constructed a vector to characterize sign language expressions. The researchers computed the similarity of sign language expressions for each part of the frame sequence and evaluated it by using characteristic actions as key images for retrieval. As a result, nine pairs were determined to be motion synonyms, seven of which had little semantic relevance and appeared infrequently, indicating that they were new motion synonyms. In their study, [PERSON] et al.