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 2.25\%/1.81\%/1.39\% on the CROHME 2014/2016/2019 datasets respectively, and achieves a remarkable 69.06\% 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，然而现有的编码器-解码器方法通常难以对$LaTeX$中的全局信息进行建模。为此，本文提出了一种新颖的隐式字符辅助学习方法，通过挖掘全局表达式信息来增强手写数学表达式识别性能。具体而言，我们设计了隐式字符构建模块来预测隐式字符序列，并利用融合模块将ICCM输出与解码器输出进行整合，从而生成修正后的预测结果。通过对隐式字符信息进行建模与利用，ICAL能够实现更精准且具有上下文感知能力的手写数学表达式解析。实验结果表明，ICAL在CROHME 2014/2016/2019数据集上的表达式识别率分别提升了2.25%/1.81%/1.39%，显著优于当前最优模型，并在具有挑战性的HME100k测试集上达到了69.06%的优异性能。相关代码已在GitHub开源：https://github.com/qingzhenduyu/ICAL