Handwritten Mathematical Expression Recognition (HMER) is a challenging task
with many educational applications. Recent methods for HMER have been developed
for complex mathematical expressions in standard horizontal format. However,
solutions for elementary mathematical expression, such as vertical addition and
subtraction, have not been explored in the literature. This work proposes a new
handwritten elementary mathematical expression dataset composed of addition and
subtraction expressions in a vertical format. We also extended the MNIST
dataset to generate artificial images with this structure. Furthermore, we
proposed a solution for offline HMER, able to recognize vertical addition and
subtraction expressions. Our analysis evaluated the object detection algorithms
YOLO v7, YOLO v8, YOLO-NAS, NanoDet and FCOS for identifying the mathematical
symbols. We also proposed a transcription method to map the bounding boxes from
the object detection stage to a mathematical expression in the LATEX markup
sequence. Results show that our approach is efficient, achieving a high
expression recognition rate. The code and dataset are available at
this https URL

手写数学表达式识别的挑战在于应用于教育，本文提出了一种新的数据集和解决方案，能够识别竖直形式的初等数学表达式，并对目标检测算法和表达式识别率进行了评估。

识别垂直加法和减法的手写数学表达式

Recognizing Handwritten Mathematical Expressions of Vertical Addition  and Subtraction

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。