Abstract:

Sign language, as the main communication channel for deaf and hearing people, plays a crucial role in daily life. With the rapid development of the field of computer vision and deep learning, the field of sign language recognition has also ushered in new opportunities. The advanced methods and technologies used in the research of sign language recognition based on computer vision in recent years are reviewed. Starting from the three branches of static sign language, isolated words and continuous sentence sign language recognition, the common methods and technical difficulties of sign language recognition are systematically explained. The steps of sign language recognition such as image preprocessing, detection and segmentation, tracking, feature extraction, and classification are introduced in detail. It summarizes and analyzes the commonly used algorithms and neural network models for sign language recognition, summarizes and organizes commonly used sign language datasets, analyzes the status quo of different sign language recognition, and finally discusses the challenges and limitations of sign language recognition.

Key words: sign language recognition, computer vision, deep learning, neural network

摘要：

手语作为聋哑人和健听人的主要交流渠道，在日常生活中发挥着十分重要的作用。随着计算机视觉领域和深度学习领域的高速发展，手语识别领域也迎来了新的机遇。对近年来基于计算机视觉的手语识别研究中使用的先进方法和技术进行了综述。从静态手语、孤立词和连续语句识别三个分支出发，系统地阐述了手语识别常用方法和技术难点。详细介绍了图像预处理、检测与分割、跟踪、特征提取、分类等手语识别步骤。总结分析了手语识别常用的算法和神经网络模型，归纳整理了常用手语数据集，并对不同语种识别现状进行了分析，探讨了手语识别面临的挑战与限制。

关键词: 手语识别, 计算机视觉, 深度学习, 神经网络