# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Datasets  
The dataset used for training and evaluation in this study was provided by KETI (Korea Electronics Technology Institute, Seongnam, Republic of Korea). It consists of a total of 12,360 video samples, divided into non-overlapping training and validation subsets with an approximate ratio of 8:2, as summarized in Table 6. The training and validation sets were used independently during model development and evaluation to ensure an unbiased assessment of model performance. Specifically, the validation set was not used during training, and the final performance was reported based on the best checkpoint selected according to validation accuracy.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Specifications** \\ \hline CPU & AMD Ryzen 9 7950X3D, 16 cores \\ \hline RAM & 128 GB DDR4 \\ \hline GPU & NVIDIA GeForce RTX 4090 \(\times\) 2, \\  & 24 GB GDDR6X \\ \hline OS & Ubuntu 22.04.2 LTS \\ \hline CUDA Version & 12.2 \\ \hline Python & 3.10.11 \\ \hline MediaPipe & 0.10.11 \\ \hline OpenCV-Python & 4.7.0.72 \\ \hline Torch & 2.0.1 \\ \hline Torchvision & 0.15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hardware and software configuration of the experimental environment.  
The dataset comprises 67 distinct classes, each representing a unique sentence related to sign language for airport directions. All videos were recorded in full HD resolution (1920 \(\times\) 1080) and feature a diverse range of sign language speakers to ensure variability in sign expressions. Pre-processing steps, including frame size standardization, normalization, and data augmentation, were performed as described in [18] to ensure consistency and facilitate model training. In addition, all videos were decomposed into individual frames, from which keypoint features (e.g., body, hand, and face landmarks) were extracted. These keypoints were subsequently transformed into images, which served as the actual input to the model. This transformation ensures both spatial and temporal consistency while providing a compact and expressive representation of sign gestures.  
Figure 1 presents example keypoint-based visualizations derived from input videos, illustrating the temporal dynamics of sign language gestures captured through body landmarks.  
In addition, to simulate real-time mobile environments, a secondary dataset was created by dropping frames uniformly at fixed intervals from the original dataset, as shown in Table 6. While the original dataset was recorded at 30 fps in a controlled server environment, real-time sign language recognition on mobile devices often operates at lower frame rates due to hardware and processing constraints. This secondary dataset maintains the same 12,360 video samples and 67 classes as the original dataset; however, the number of frames per video is systematically reduced. By lowering the frame rate while preserving the original class labels and dataset structure, the secondary dataset provides a more realistic representation of real-world mobile scenarios, ensuring robustness during deployment under varying frame rate conditions.