| 总结: | Handwritten Mathematical Expressions Recognition (HMER) is a crucial problem in the field of
artificial intelligence and machine learning, given the complexity and variability of handwriting
in two dimensions. Existing approaches to HMER face challenges such as handwriting style
variability, non-standard symbols and notation, and errors and ambiguities in writing. In this
study, we propose a novel approach to HMER using rotary position embeddings and a hybrid
loss calculation of connectionist temporal classification and cross entropy to improve the
accuracy of transformer-based models for recognizing cursive handwriting and complex
equations. We train and test our approach on public databases from CHROHME 2014, 2016, and
2019 of offline HMEs. Our experiments demonstrate that our approach results in higher
expression recognition rates and lower word error counts compared to existing approaches.
Notably, our results are comparable to recent studies in the field, highlighting the potential of our
approach to advance the state of the art in HMER.
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