This research paper describes a realtime system for identifying American Sign Language (ASL) movements that employs modern computer vision and machine learning approaches. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification. The testing results show that the suggested system can detect all ASL alphabets with an accuracy of 99.95%, indicating its potential for use in communication devices for people with hearing impairments. The proposed approach can also be applied to additional sign languages with similar hand motions, potentially increasing the quality of life for people with hearing loss. Overall, the study demonstrates the effectiveness of using Mediapipe and CNN for real-time sign language recognition, making a significant contribution to the field of computer vision and machine learning.

翻译：本研究论文描述了一套结合现代计算机视觉与机器学习方法的实时美国手语（ASL）动作识别系统。所提出的方法利用Mediapipe库进行特征提取，并采用卷积神经网络（CNN）实现ASL手势分类。测试结果表明，该系统能以99.95%的准确率识别全部ASL字母，展现了其在听力障碍人士通讯设备中的应用潜力。该方案还可推广至其他具有相似手部动作的手语系统，有望提升听力受损人群的生活质量。总体而言，本研究验证了将Mediapipe与CNN运用于实时手语识别的有效性，为计算机视觉与机器学习领域作出了重要贡献。