## 6 Conclusions  
In this paper, we applied a ViT to a relatively small dataset of 20 \(\times\) 40 dimensions. As discussed in Section 5, The ViT model achieves its best accuracy with four encoder layers experiences a decrease in accuracy as the number of layers increases beyond four. Particularly, accuracy significantly declined when the number of encoder layers exceeded ten, suggesting that a smaller dataset, like the one used in this experiment, limits the number of features. Accuracy did not significantly change from three to four encoder layers, only seeing subtle improvement. Future research should increase the dataset. Therefore, when creating additional datasets, it is necessary to increase the amount of participants who speak sign language and capture videos of finger spelling movements from various angles.  
For finger spelling without actions, both the ViT and CNN models were able to achieve high accuracy with a small amount of training. However, for finger spelling with actions, the accuracy was poor. This is thought to be because the finger shape in finger spelling with actions is very similar to that of finger spelling without actions due to the narrowing of the number of features and the number of frames. For example, the finger shape of 'no(O)' is very similar to that of 'hi(O)' at the beginning of the action and'so(x)' at the end of the action, and the ViT model misidentified these two finger shapes.  
Therefore, in future research, the number of extracted frames and angular features should be increased, and postures and facial expressions should also be included as features to improve the accuracy in real time. Based on the results of this study, the ViT with 20-dimensional data (Dataset 2) showed the highest accuracy. In real-time recognition, the accuracy was lower than that of the CNN model, but the accuracy of each character was significantly different, suggesting that accuracy can be greatly improved by studying the learning method. Therefore, we believe that accuracy can be improved by increasing the number of frames, the number of angular features for each joint of the upper body us  
Figure 7: Comparison of ViT and CNN recognition performance on sign languages with and without movement.  
ing MediaPipe's Holistic, and the number of features for the entire mouth and eye area of the face.  
Finally, we discuss some issues for future research. In this experiment, the dataset was small because there were only two people who used sign language on a regular basis that were available. Therefore, we would like to expand the dataset by increasing the number of participants in future experiments.  
Conceptualization, D.S. and Y.K.; Methodology, Y.K.; Validation, Y.K.; Formal analysis, S.N.; Data curation, Z.H.; Writing\(-\)original draft, T.K. and Y.K.; Writing\(-\)review & editing, D.S.; Visualization, T.K.; Supervision, Y.K.; Project administration, Y.K.; Funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Co-G.E.I. (Cooperative Good Educational Innovation) Challenge 2023 of Tokyo Polytechnic University.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Tokyo Polytechnic University (2022-01).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
We would like to express our sincere gratitude to the two signers who helped us create the Japanese sign language database.  
The authors declare no conflict of interest.