Significant progress has been made in the field of handwritten mathematicalexpression recognition, while existing encoder-decoder methods are usuallydifficult to model global information in $LaTeX$LaTeX. Therefore, this paperintroduces a novel approach, Implicit Character-Aided Learning (ICAL), to minethe global expression information and enhance handwritten mathematicalexpression recognition. Specifically, we propose the Implicit CharacterConstruction Module (ICCM) to predict implicit character sequences and use aFusion Module to merge the outputs of the ICCM and the decoder, therebyproducing corrected predictions. By modeling and utilizing implicit characterinformation, ICAL achieves a more accurate and context-aware interpretation ofhandwritten mathematical expressions. Experimental results demonstrate thatICAL notably surpasses the state-of-the-art(SOTA) models, improving theexpression recognition rate (ExpRate) by 2.25%/1.81%/1.39% on the CROHME2014/2016/2019 datasets respectively, and achieves a remarkable 69.06% on thechallenging HME100k test set. We make our code available on the GitHub:https://github.com/qingzhenduyu/ICAL