This paper presents the UniMER dataset to provide the first study on
Mathematical Expression Recognition (MER) towards complex real-world scenarios.
The UniMER dataset consists of a large-scale training set UniMER-1M offering an
unprecedented scale and diversity with one million training instances and a
meticulously designed test set UniMER-Test that reflects a diverse range of
formula distributions prevalent in real-world scenarios. Therefore, the UniMER
dataset enables the training of a robust and high-accuracy MER model and
comprehensive evaluation of model performance. Moreover, we introduce the
Universal Mathematical Expression Recognition Network (UniMERNet), an
innovative framework designed to enhance MER in practical scenarios. UniMERNet
incorporates a Length-Aware Module to process formulas of varied lengths
efficiently, thereby enabling the model to handle complex mathematical
expressions with greater accuracy. In addition, UniMERNet employs our UniMER-1M
data and image augmentation techniques to improve the model's robustness under
different noise conditions. Our extensive experiments demonstrate that
UniMERNet outperforms existing MER models, setting a new benchmark in various
scenarios and ensuring superior recognition quality in real-world applications.
The dataset and model are available at
this https URL

该研究提供了 UniMER 数据集，对复杂的实际场景中的数学表达式识别进行了首次研究。UniMER 数据集由大规模训练集 UniMER-1M 和精心设计的测试集 UniMER-Test 组成，使得能够训练出鲁棒和高准确性的数学表达式识别模型并全面评估其性能。此外，研究还介绍了 Universal Mathematical Expression Recognition Network (UniMERNet) 的创新框架，以提升实际场景下的数学表达式识别。经过广泛实验验证，UniMERNet 在各种场景中表现优于现有的模型，为真实世界应用提供了卓越的识别质量。