  Printed Mathematical expression recognition (PMER) aims to transcribe a
printed mathematical expression image into a structural expression, such as
LaTeX expression. It is a crucial task for many applications, including
automatic question recommendation, automatic problem solving and analysis of
the students, etc. Currently, the mainstream solutions rely on solving image
captioning tasks, all addressing image summarization. As such, these methods
can be suboptimal for solving MER problem.
  In this paper, we propose a new method named EDSL, shorted for
encoder-decoder with symbol-level features, to identify the printed
mathematical expressions from images. The symbol-level image encoder of EDSL
consists of segmentation module and reconstruction module. By performing
segmentation module, we identify all the symbols and their spatial information
from images in an unsupervised manner. We then design a novel reconstruction
module to recover the symbol dependencies after symbol segmentation.
Especially, we employ a position correction attention mechanism to capture the
spatial relationships between symbols. To alleviate the negative impact from
long output, we apply the transformer model for transcribing the encoded image
into the sequential and structural output. We conduct extensive experiments on
two real datasets to verify the effectiveness and rationality of our proposed
EDSL method. The experimental results have illustrated that EDSL has achieved
92.7\% and 89.0\% in evaluation metric Match, which are 3.47\% and 4.04\%
higher than the state-of-the-art method. Our code and datasets are available at
https://github.com/abcAnonymous/EDSL .
