MFH: Marrying Frequency Domain with Handwritten Mathematical Expression Recognition

TL;DR

This paper introduces MFH, a novel approach that integrates frequency domain analysis using DCT into handwritten mathematical expression recognition, significantly improving accuracy across multiple datasets.

Contribution

It is the first to incorporate frequency domain information into HMER, enhancing structural analysis and recognition performance of mathematical formulas.

Findings

Achieved accuracy rates of 61.66%, 62.07%, and 63.72% on CROHME 2014/2016/2019 datasets.

Consistent performance improvements across various baseline models.

Demonstrated the effectiveness of frequency domain features in complex formula recognition.

Abstract

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.