Abstract
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 this https URL.
Abstract (translated)
该研究旨在通过使用先进的人工智能技术开发一个准确的巴勒斯坦手语（PSL）识别系统，提高听力受损学生的数学教育可及性。由于缺乏数字资源，创建了一个包含41个数学手势类别的定制数据集，并由PSL专家录制以确保语言准确性与领域专属性。为了利用最先进的计算机视觉技术，对Vision Transformer (ViT) 模型进行了微调用于手势分类。该模型实现了97.59%的准确率，证明了其在识别数学符号时具有高精度和可靠性。本研究强调深度学习在开发智能教育工具中的作用，这些工具通过提供AI驱动的互动解决方案来缩小听力受损学生的学业差距，从而增强他们的数学理解能力。这项工作标志着向创新和包容性数字化整合专业学习环境迈出的重要一步。数据集托管于Hugging Face平台上的此链接：[请参见原文]。 注意：原始文本中的URL无法直接复制粘贴到翻译结果中，请访问原始文档获取具体链接地址。
URL
https://arxiv.org/abs/2505.17055
