DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for
  HMER
Aniket Pal Krishna Pratap Singh
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.