用频域分析提升手写公式识别准确率
MFH: Marrying Frequency Domain with Handwritten Mathematical Expression Recognition
- 将离散余弦变换引入手写公式识别，利用频域信息增强结构理解
- 在CROHME数据集上达到61.66%~63.72%的识别准确率
- 方法可适配多种模型，适合做公式识别的开发者参考
手写数学公式识别（HMER）因公式结构复杂、字符排列不规则，在序列预测中面临挑战。本文提出将频域分析融入HMER的方法（MFH），利用离散余弦变换（DCT）提取频域特征，强化对公式结构的判别能力。在多个基线模型上，该方法均实现一致性能提升，验证了频域信息的有效性。实验表明，MFH-CoMER在CROHME 2014/2016/2019测试集上分别取得61.66%/62.07%/63.72%的准确率。代码已开源。
原文摘要 · Abstract (English)
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.
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