In this paper, we provide a review of Mathematical Expression Recognition (MER) for both printed and handwritten domains. We describe the past work in a manner that clearly highlights the common and distinguishing aspects. The review reveals advances in the techniques used for the detection and recognition of MEs for online and offline modalities. Systems have evolved from the use of domain-specific crafted modules to architectures learned in a completely data-driven manner. Learning tasks and datasets have been standardized, thus allowing performance comparison of the systems. With automated learning of improved feature representations and guided by the related fields such as machine translation and captioning, the systems have progressed to demonstrate improved robustness to the variability in the inputs.

在本文中，我们对印刷和手写领域的数学表达式识别 (MER) 进行了综述。我们以明确突出共同点和区别点的方式描述过去的工作。该评论揭示了用于在线和离线模式的 ME 检测和识别的技术的进步。系统已经从使用特定领域的精心制作的模块发展到以完全数据驱动的方式学习的架构。学习任务和数据集已经标准化，因此可以对系统进行性能比较。通过自动学习改进的特征表示并在机器翻译和字幕等相关领域的指导下，系统已经取得进展，以证明对输入可变性的改进鲁棒性。