Mathematics education, a crucial and basic field, significantly influences students' learning in related subjects and their future careers. Utilizing artificial intelligence to interpret and comprehend math problems in education is not yet fully explored. This is due to the scarcity of quality datasets and the intricacies of processing handwritten information. In this paper, we present a novel contribution to the field of mathematics education through the development of MNIST-Fraction, a dataset inspired by the renowned MNIST, specifically tailored for the recognition and understanding of handwritten math fractions. Our approach is the utilization of deep learning, specifically Convolutional Neural Networks (CNNs), for the recognition and understanding of handwritten math fractions to effectively detect and analyze fractions, along with their numerators and denominators. This capability is pivotal in calculating the value of fractions, a fundamental aspect of math learning. The MNIST-Fraction dataset is designed to closely mimic real-world scenarios, providing a reliable and relevant resource for AI-driven educational tools. Furthermore, we conduct a comprehensive comparison of our dataset with the original MNIST dataset using various classifiers, demonstrating the effectiveness and versatility of MNIST-Fraction in both detection and classification tasks. This comparative analysis not only validates the practical utility of our dataset but also offers insights into its potential applications in math education. To foster collaboration and further research within the computational and educational communities. Our work aims to bridge the gap in high-quality educational resources for math learning, offering a valuable tool for both educators and researchers in the field.

翻译：数学教育作为一个关键且基础的领域，显著影响着学生对相关学科的学习及其未来职业发展。利用人工智能来解读和理解教育中的数学问题尚未得到充分探索，这主要是由于高质量数据集的稀缺以及手写信息处理的复杂性。本文通过开发MNIST-Fraction数据集，为数学教育领域做出了新颖贡献。该数据集受著名的MNIST数据集启发，专门为手写数学分数的识别与理解而定制。我们的方法是利用深度学习，特别是卷积神经网络（CNNs），来识别和理解手写数学分数，从而有效检测和分析分数及其分子与分母。这种能力对于计算分数值至关重要，而分数计算是数学学习的基本方面。MNIST-Fraction数据集旨在紧密模拟真实场景，为人工智能驱动的教育工具提供可靠且相关的资源。此外，我们使用多种分类器将我们的数据集与原始MNIST数据集进行了全面比较，证明了MNIST-Fraction在检测和分类任务中的有效性和多功能性。这一比较分析不仅验证了我们数据集的实用价值，还揭示了其在数学教育中的潜在应用前景。为了促进计算与教育领域的合作与进一步研究，我们的工作旨在弥合数学学习高质量教育资源的缺口，为该领域的教育工作者和研究人员提供有价值的工具。