# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 5 Conclusions  
Sign language recognition (SLR) services have become increasingly important in recent years, driven by the growing need for inclusive communication in both public and private sectors. As advancements in deep learning and computer vision continue, efficient and accurate SLR systems are vital for enabling real-time communication, especially in resource-constrained environments. In this study, we proposed a dynamic clip generation method to enhance the efficiency and accuracy of sign language recognition (SLR). By adaptively adjusting the number of clips based on our proposed coverage metric, our method overcomes the limitations of traditional fixed clip generation methods, which use a predetermined number of clips regardless of video length. Unlike these conventional method, our dynamic method ensures that longer videos retain sufficient contextual information while preventing redundant processing in shorter videos. This optimal balance between computational efficiency and recognition accuracy makes our method particularly valuable for real-time SLR applications, especially in resource-constrained environments. Experimental results across multiple model architectures demonstrated that the optimal number of clips varied by model: 5 clips for 3D CNNs, 6 clips for the R(2+1)D and Video Swin Transformer (VST), and 4 clips for Multiscale Vision Transformers (MViTs). Our dynamic method successfully reduced inference time while maintaining or even improving accuracy compared to fixed methods. Specifically, we achieved inference time reductions of 12.50% for 3D CNNs, 27.78% for R(2+1)D, and 28.57% for VSTs, with accuracy remaining within a 0.5% margin or better than the fixed method. Notably, VSTs saw a 28.57% reduction in inference time, with the dynamic method slightly outperforming the fixed method in accuracy (98.67% vs. 98.62%). For MViTs, both methods showed similar performance, suggesting that the effectiveness of dynamic clip generation may depend on the model's architecture and temporal processing capabilities. Our analysis revealed that when the coverage value fell below 1.0, recognition accuracy declined significantly due to insufficient contextual information, confirming the importance of our coverage metric as a reliable indicator for clip generation decisions. Additionally, we observed that excessive clip generation (coverage values significantly above 1.0) sometimes led to redundancy, negatively impacting both computational efficiency and recognition accuracy, as seen in the performance degradation of 3D CNNs and MViTs with higher clip counts. In conclusion, this work represents an important step toward more efficient and accurate sign language recognition systems that can operate effectively across different computational platforms. By dynamically adjusting clip generation based on contextual requirements, our method offers significant improvements in both inference time and accuracy, contributing to the development of more robust and scalable SLR systems. Conceptualization, T.K. and B.K. ; methodology, T.K. and B.K. ; software, T.K. ; validation, T.K. and B.K. ; formal analysis, T.K. and B.K. ; investigation, T.K. and B.K. ; resources, T.K. and B.K. ; data curation, T.K. and B.K. ; writing--original draft preparation, T.K. and B.K. ; writing--review and editing, T.K. and B.K. ; visualization, T.K. and B.K. ; supervision, B.K. ; project administration, B.K. ; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2022-II20043, Adaptive Personality for Intelligent Agents) (RS-2025-02263917, Development of Digital Agent-Society Platform for Training Human-Centric AGI Agents)  
**Informed Consent Statement:** Not applicable. **Data Availability Statement:** All data sources and experimental results are provided within the manuscript.