Hierarchical and complex Mathematical Expression Recognition (MER) is challenging due to multiple possible interpretations of a formula, complicating both parsing and evaluation. In this paper, we introduce the Hierarchical Detail-Focused Recognition dataset (HDR), the first dataset specifically designed to address these issues. It consists of a large-scale training set, HDR-100M, offering an unprecedented scale and diversity with one hundred million training instances. And the test set, HDR-Test, includes multiple interpretations of complex hierarchical formulas for comprehensive model performance evaluation. Additionally, the parsing of complex formulas often suffers from errors in fine-grained details. To address this, we propose the Hierarchical Detail-Focused Recognition Network (HDNet), an innovative framework that incorporates a hierarchical sub-formula module, focusing on the precise handling of formula details, thereby significantly enhancing MER performance. Experimental results demonstrate that HDNet outperforms existing MER models across various datasets.

翻译：层次化复杂数学表达式识别（MER）因公式存在多种可能解释而具有挑战性，这使解析和评估都变得复杂。本文介绍了层次化细节聚焦识别数据集（HDR），这是首个专门为解决这些问题而设计的数据集。它包含一个大规模训练集HDR-100M，提供了一亿个训练实例，具有前所未有的规模和多样性。测试集HDR-Test则包含复杂层次化公式的多种解释，用于全面的模型性能评估。此外，复杂公式的解析常受细粒度细节错误的影响。为此，我们提出了层次化细节聚焦识别网络（HDNet），这是一个创新框架，它整合了一个层次化子公式模块，专注于精确处理公式细节，从而显著提升了MER性能。实验结果表明，HDNet在多个数据集上均优于现有的MER模型。