# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 3 Stacking of Distilled Vision Transformers (SDViT)
### Stacking
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **ASL Dataset** & **ASL with Digits Dataset** & **NUS Hand Gesture Dataset** & **Average Accuracy** \\ \hline Deep learning with CNN [41] & 99.96 & - & 94.70 & - \\ CNN Baseline A [42] & 99.85 & 98.69 & 89.15 & 95.90 \\ CNN Baseline B [43] & 99.78 & 98.65 & 89.30 & 95.91 \\ ADCNN [26] & 98.50 & 98.49 & 83.10 & 93.36 \\ DAG-CNN [44] & 99.89 & 98.13 & 91.05 & 96.36 \\ EdenseNet [6] & 99.89 & 98.85 & 96.75 & 98.50 \\ CNN-SPP [3] & 99.94 & 99.17 & 95.95 & 98.35 \\ SDViT (Ours) & 100.00 & 99.60 & 100.00 & 99.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy percentages for classification (%) of the SOTA methods and the proposed SDViT method across the three hand gesture datasets. puter vision tasks, offering improvements in performance, efficiency, and interpretability, thus showcasing its potential for broader applications. In our extensive evaluations, the proposed SDViT consistently outperformed existing methods on three benchmark datasets. Achieving a remarkable testing accuracy of 100.00% on the ASL dataset, 99.60% on the ASL with digits dataset, and 100.00% on the NUS hand gesture dataset and an average accuracy of 99.87% across all three datasets, the results underscore the model's robustness and effectiveness. The superior performance positions SDViT as a state-of-the-art solution for vision-based static hand gesture recognition, demonstrating its potential for real-world applications across diverse domains within computer vision. Conceptualization, C.K.T. and K.M.L. ; methodology, C.K.T., K.M.L., C.P.L., R.K.Y.C. and A.A.; software, C.K.T. and K.M.L. ; validation, C.K.T., K.M.L., C.P.L., R.K.Y.C. and A.A.; formal analysis, C.K.T. ; investigation, C.K.T. and K.M.L. ; resources, C.K.T. and K.M.L. ; data curation, C.K.T. and C.P.L. ; writing--original draft preparation, C.K.T. and K.M.L. ; writing--review and editing, C.K.T., K.M.L., C.P.L., R.K.Y.C. and A.A.; visualization, C.K.T. and C.P.L. ; supervision, K.M.L. and R.K.Y.C. ; project administration, K.M.L. and A.A.; funding acquisition, K.M.L. and A.A. All authors have read and agreed to the published version of the manuscript. The research in this work was supported by Telekom Malaysia Research & Development under grant number RDTC/231084 and by the Deanship of Scientific Research, King Khalid University, Saudi Arabia, under grant number RGP2/332/44. Not applicable. Data Availability StatementData are contained within the article. The authors declare no conflict of interest. ## References

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