近年來，聽障人士的人口逐漸增長，大眾對於手語學習的需求也跟 著逐年提升，然而，手語學習的困難度高，且學習資源有限，使得手語 學習成為一個困難的任務。 為了解決這個問題，本論文提出了一種基於通道拓樸增強圖卷積神 經網絡（CTRGCN）的基於骨架手語單詞辨識演算法。本研究針對手語 單詞辨識，設計了改良的CTRGCN 模型，並提出多分支的架構，以提高 辨識準確度。我們使用WLASL100 數據集進行訓練，並與現有模型進行 了的比較。結果顯示，我們的方法在多數情境下均優於現有技術，展示 了其在手語單詞辨識上的潛力和實用性，並希望為手語學習提供更多的 幫助。;In recent years, the population of hearing-impaired individuals has been gradually increasing, and the public’s demand for sign language learning has been steadily rising as well. However, the difficulty of learning sign language is high, and the learning resources are limited, making it a relatively challenging task. To address this issue, this paper proposes a Skeleton based sign language word recognition algorithm based on Channel-Topology Refinement Graph Convolutional Network (CTRGCN). This method tackles the challenges in sign language word recognition, by designing an improved CTRGCN model to enhance recognition accuracy. We trained the model using the WLASL100 dataset and compared it with existing models. The results demonstrate that our method outperforms existing techniques in most scenarios, showcasing its potential and practicality in sign language word recognition. We hope to provide more assistance for sign language learning through this approach.
