MFH: Marrying Frequency Domain with Handwritten Mathematical Expression Recognition
This addresses recognition challenges in complex mathematical formulas for researchers and practitioners, but it is incremental as it builds on existing models.
The paper tackles handwritten mathematical expression recognition by incorporating frequency domain analysis via discrete cosine transform, achieving accuracy rates of 61.66% to 63.72% on CROHME test sets.
Handwritten mathematical expression recognition (HMER) suffers from complex formula structures and character layouts in sequence prediction. In this paper, we incorporate frequency domain analysis into HMER and propose a method that marries frequency domain with HMER (MFH), leveraging the discrete cosine transform (DCT). We emphasize the structural analysis assistance of frequency information for recognizing mathematical formulas. When implemented on various baseline models, our network exhibits a consistent performance enhancement, demonstrating the efficacy of frequency domain information. Experiments show that our MFH-CoMER achieves noteworthy accuracyrates of 61.66%/62.07%/63.72% on the CROHME 2014/2016/2019 test sets. The source code is available at https://github.com/Hryxyhe/MFH.