  The use of artificial intelligence technology in education is growing
rapidly, with increasing attention being paid to handwritten mathematical
expression recognition (HMER) by researchers. However, many existing methods
for HMER may fail to accurately read formulas with complex structures, as the
attention results can be inaccurate due to illegible handwriting or large
variations in writing styles. Our proposed Intelligent-Detection Network (IDN)
for HMER differs from traditional encoder-decoder methods by utilizing object
detection techniques. Specifically, we have developed an enhanced YOLOv7
network that can accurately detect both digital and symbolic objects. The
detection results are then integrated into the bidirectional gated recurrent
unit (BiGRU) and the baseline symbol relationship tree (BSRT) to determine the
relationships between symbols and numbers. The experiments demonstrate that the
proposed method outperforms those encoder-decoder networks in recognizing
complex handwritten mathematical expressions. This is due to the precise
detection of symbols and numbers. Our research has the potential to make
valuable contributions to the field of HMER. This could be applied in various
practical scenarios, such as assignment grading in schools and information
entry of paper documents.
