# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 1 Introduction  
Sign language (SL) is an essential means of communication for people with hearing impairments and serves as a bridge between the Deaf and non-Deaf communities. With recent advances in artificial intelligence, deep learning-based sign language recognition (SLR) has gained significant attention, leading to the development of various translation systems [1, 2, 3, 4, 5]. In addition to these developments, recent studies have explored sign language recognition in mobile environments, making communication more accessible and convenient [6, 7, 8, 9]. While these technologies enhance communication accessibility, research on optimizing and improving the performance of sign language recognition systems remains limited, particularly in terms of model recognition accuracy and inference speed. To enhance the accuracy of sign language recognition (SLR), videos are commonly segmented into multiple clips for model processing. This multi-clip approach is widelyadopted across video recognition as it demonstrably optimizes computational efficiency and recognition accuracy by enabling models to effectively capture temporal dynamics within manageable computational constraints [10, 11, 12]. In deep learning-based SLR systems, this segmentation addresses a fundamental requirement: these models typically operate on fixed-length sequences of image frames as input. Consequently, dividing videos into multiple segments becomes necessary to conform to these architectural constraints. Nonetheless, conventional approaches that segment videos into a predetermined and fixed number of clips present inherent limitations. In the case of longer sign language videos, such fixed partitioning often leads to excessive frame skipping, thereby omitting essential gestural cues crucial for accurate recognition. Conversely, shorter videos may necessitate frame duplication to meet input length requirements, introducing redundancy and degrading model performance. These constraints underscore the necessity for a more adaptive and content-aware clip generation strategy that can dynamically adjust to the temporal characteristics of each video. To address the aforementioned limitations, we propose a dynamic clip generation method that adjusts the number of clips during inference based on a novel coverage metric. This metric reflects how effectively temporal information is captured from the video. Our method dynamically determines the number of clips based on video length, ensuring sufficient context for long videos while avoiding redundancy in short ones. While a full discussion of model-specific performance is provided in the conclusion, the proposed dynamic clip generation method enhances sign language recognition accuracy while minimizing inference time overhead. The main contributions of this study are as follows:  
* Dynamic Clip Generation Based on Coverage: We propose a novel dynamic clip generation method that adjusts the number of clips during inference based on a proposed coverage metric. This metric reflects how effectively temporal information is captured from sign language videos, enabling adaptive clip generation tailored to each video's length and temporal complexity. * Improved Trade-off Between Accuracy and Inference Time: Through extensive experiments on various SLR models, including 3D CNN, R(2+1)D, Video Swin Transformer (VST), and Multiscale Vision Transformers (MViT), we demonstrate that our dynamic method achieves a better trade-off between computational efficiency and recognition accuracy compared to fixed clip generation methods. * Empirical Validation of the Coverage Metric: We conduct a quantitative analysis by varying the number of clips and measuring the corresponding coverage values. The results show that maintaining a coverage value of at least 1 is essential for preserving contextual information, while excessive redundancy can be avoided through our dynamic method. * General Applicability Across Architectures: Our method consistently improves or maintains performance across a range of model architectures.