Significant progress has been made in the field of handwritten mathematical expression recognition, while existing encoder-decoder methods are usually difficult to model global information in \LaTeX. Therefore, this paper introduces a novel approach, Implicit Character-Aided Learning (ICAL), to mine the global expression information and enhance handwritten mathematical expression recognition. Specifically, we propose the Implicit Character Construction Module (ICCM) to predict implicit character sequences and use a Fusion Module to merge the outputs of the ICCM and the decoder, thereby producing corrected predictions. By modeling and utilizing implicit character information, ICAL achieves a more accurate and context-aware interpretation of handwritten mathematical expressions. Experimental results demonstrate that ICAL notably surpasses the state-of-the-art(SOTA) models, improving the expression recognition rate (ExpRate) by 2.21\%/1.75\%/1.28\% on the CROHME 2014/2016/2019 datasets respectively, and achieves a remarkable 69.25\% on the challenging HME100k test set. We make our code available on the GitHub: https://github.com/qingzhenduyu/ICAL

翻译：手写数学表达式识别领域已取得显著进展，但现有编码器-解码器方法通常难以建模\LaTeX中的全局信息。为此，本文提出一种新方法——隐式字符辅助学习（ICAL），旨在挖掘全局表达式信息并增强手写数学表达式识别。具体而言，我们提出隐式字符构建模块（ICCM）来预测隐式字符序列，并使用融合模块合并ICCM与解码器的输出，从而生成修正后的预测。通过建模和利用隐式字符信息，ICAL实现了对手写数学表达式更准确且更具上下文感知能力的解读。实验结果表明，ICAL显著超越了当前最优（SOTA）模型，在CROHME 2014/2016/2019数据集上分别将表达式识别率（ExpRate）提升了2.21%/1.75%/1.28%，并在具有挑战性的HME100k测试集上达到了69.25%的优异表现。我们在GitHub上公开了代码：https://github.com/qingzhenduyu/ICAL