用AI识别巴勒斯坦手语，帮听力障碍者学数学
Enhancing Mathematics Learning for Hard-of-Hearing Students Through Real-Time Palestinian Sign Language Recognition: A New Dataset
- 用Vision Transformer模型识别41类数学手语
- 手语识别准确率达97.59%
- 专为听障学生打造的数学教育工具
本研究旨在通过先进的深度学习技术，构建一个高精度的巴勒斯坦手语（PSL）识别系统，以提升听力障碍学生的数学教育可及性。由于现有数字化资源匮乏，研究团队采集并构建了一个包含41个数学手势类别的定制数据集，由专业PSL使用者录制，确保语言准确性与领域针对性。基于此数据集，对Vision Transformer（ViT）模型进行微调，用于手势分类，最终达到97.59%的识别准确率，证明该系统在高精度、高可靠性识别数学手语方面的有效性。该研究展示了深度学习在开发智能教育工具中的潜力，能够通过人工智能驱动的交互式解决方案，弥合听障学生的知识鸿沟。研究成果推动了特殊教育环境中创新且包容的数字融合进程。数据集已发布于Hugging Face：https://huggingface.co/datasets/fidaakh/STEM_data。
原文摘要 · Abstract (English)
The study aims to enhance mathematics education accessibility for hard-of-hearing students by developing an accurate Palestinian sign language PSL recognition system using advanced artificial intelligence techniques. Due to the scarcity of digital resources for PSL, a custom dataset comprising 41 mathematical gesture classes was created, and recorded by PSL experts to ensure linguistic accuracy and domain specificity. To leverage state-of-the-art-computer vision techniques, a Vision Transformer ViTModel was fine-tuned for gesture classification. The model achieved an accuracy of 97.59%, demonstrating its effectiveness in recognizing mathematical signs with high precision and reliability. This study highlights the role of deep learning in developing intelligent educational tools that bridge the learning gap for hard-of-hearing students by providing AI-driven interactive solutions to enhance mathematical comprehension. This work represents a significant step toward innovative and inclusive frosting digital integration in specialized learning environments. The dataset is hosted on Hugging Face at https://huggingface.co/datasets/fidaakh/STEM_data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。