  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
https://github.com/opendatalab/UniMERNet.
