# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 5 Conclusions and Future Work  
The Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) are a game-changer in sign language sentence recognition (SLSR). This innovative frame  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Methodology** & **Accuracy (\%)** & **Inference Time (ms)** & **Number of Parameters (Million)** \\ \hline HGE-ACN (Proposed) & Hybrid Graph Embedding + Adaptive Convolution & 94.8\% & 45 ms & 12.5 M \\ DDSTGCNN [1] & Dynamic Dense Spatiotemporal & 89.2\% & 50 ms & 14.3 M \\  & Graph CNN & & & \\ MSeqGraph [2] & Multimodal Sequential & 91.1\% & 56 ms & 15.2 M \\  & Graph Embedding & & & \\ BAE [3] & Boundary-Adaptive Encoder for & 87.4\% & 62 ms & 11.8 M \\  & Sign Language & & & \\ SKResNet-TCN [4] & Hybrid ResNet + Temporal & 92.3\% & 48 ms & 13.1 M \\  & Convolution & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with recent methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Learning Rate** & **Batch Size** & **Number of Layers** & **Dropout Rate** & **Optimizer** \\ \hline HGE-ACN (Proposed) & 0.001 & 64 & 3 GCN + 2 ACN & 0.4 & AdamW \\ DDSTGCNN & 0.0015 & 64 & 4 GCN & 0.5 & Adam \\ MSeqGraph & 0.002 & 32 & 2 GCN + 1 LSTM & 0.3 & RMSprop \\ BAE & 0.001 & 32 & 2 LSTM & 0.3 & Adam \\ SKResNet-TCN & 0.0008 & 64 & 3 ResNet + 2 TCN & 0.4 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings for benchmark models.  
work combines graph-based embeddings with Adaptive Convolutional Networks to address the challenges of decoding complex sign language communication. Among the significant achievements are the following: constructing a robust SLSR system using a wearable glove with six-axis inertial and five-finger curvature sensors; demonstrating that hybrid modeling techniques can improve recognition accuracy; and lastly, developing a versatile and adaptive recognition framework capable of surpassing the shortcomings of existing deep learning models. There are significant implications for accessibility, communication technology, and inclusive design, as stated in the paper. The proposed method has the potential to improve communication tools for the deaf population by improving machine understanding of sign language, which in turn can change platforms for social and professional, as well as educational, interactions. The next stages should center on further studies and finding practical uses for the HGE-ACN technique. Some of these areas of focus include investigating interactions between humans and computers in the context of adaptive communication, gesture-based interface design, rehabilitation efforts, and therapy technologies; incorporating advanced machine learning techniques; optimizing deployment for real-world use; building context-aware sign language interpretation systems; and expanding the current framework to support recognition across different sign languages. Building on HGE-ACN's work in these areas, further research can improve sign language recognition algorithms to make them more accessible and valuable for a wider range of users.  
Conceptualization, Y.L. and C.J.; methodology, Y.L. and C.J.; software, Y.L.; validation, Y.L. and P.C.; resources, P.C.; writing--original draft preparation, Y.L. and PC.; writing--review and editing, Y.L. and C.J.; formal analysis, Y.L.; supervision, C.J.; project administration, P.C.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Srinakharinwirot University Research Fund.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors wish to gratefully acknowledge financial support for this research from the Srinakharinwirot University Research fund.  
The authors declare no conflicts of interest.