The task of recognising Handwritten Mathematical Expressions (HMER) is
crucial in the fields of digital education and scholarly research. However, it
is difficult to accurately determine the length and complex spatial
relationships among symbols in handwritten mathematical expressions. In this
study, we present a novel encoder-decoder architecture (DenseBAM-GI) for HMER,
where the encoder has a Bottleneck Attention Module (BAM) to improve feature
representation and the decoder has a Gated Input-GRU (GI-GRU) unit with an
extra gate to make decoding long and complex expressions easier. The proposed
model is an efficient and lightweight architecture with performance equivalent
to state-of-the-art models in terms of Expression Recognition Rate (exprate).
It also performs better in terms of top 1, 2, and 3 error accuracy across the
CROHME 2014, 2016, and 2019 datasets. DenseBAM-GI achieves the best exprate
among all models on the CROHME 2019 dataset. Importantly, these successes are
accomplished with a drop in the complexity of the calculation and a reduction
in the need for GPU memory.

本研究提出了一种新颖的编码器解码器结构（DenseBAM-GI）来识别手写数学表达式。该模型具有 Bottleneck Attention Module（BAM）以提高特征表示和带有额外门的 Gated Input-GRU（GI-GRU）单元的解码器，可轻松解码长且复杂的表达式。在 CROHME 2014、2016 和 2019 数据集上，DenseBAM-GI 取得了最佳 expression recognition rate，而且模型复杂度低且减少了 GPU 内存的需求。

具有动量辅助 GRU 的注意力增强 DeneseNet 用于 HMER

DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for  HMER

Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging
problem because of the ambiguity and complexity of two-dimensional handwriting.
Moreover, the lack of large training data is a serious issue, especially for
academic recognition systems. In this paper, we propose pattern generation
strategies that generate shape and structural variations to improve the
performance of recognition systems based on a small training set. For data
generation, we employ the public databases: CROHME 2014 and 2016 of online
HMEs. The first strategy employs local and global distortions to generate shape
variations. The second strategy decomposes an online HME into sub-online HMEs
to get more structural variations. The hybrid strategy combines both these
strategies to maximize shape and structural variations. The generated online
HMEs are converted to images for offline HME recognition. We tested our
strategies in an end-to-end recognition system constructed from a recent deep
learning model: Convolutional Neural Network and attention-based
encoder-decoder. The results of experiments on the CROHME 2014 and 2016
databases demonstrate the superiority and effectiveness of our strategies: our
hybrid strategy achieved classification rates of 48.78% and 45.60%,
respectively, on these databases. These results are competitive compared to
others reported in recent literature. Our generated datasets are openly
available for research community and constitute a useful resource for the HME
recognition research in future.

本文提出了利用 CROHME 数据库的在线手写数学表达式，通过局部和全局扭曲以及分解策略以生成形状和结构变化，使用深度学习模型， 结合卷积神经网络和基于注意力机制的编码器 - 解码器结构进行端到端的在线手写数学表达式识别，取得了具有竞争力的结果，并且产生的数据集已公开。