Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network
<|reference_start|>Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network: Hierarchical and complex Mathematical Expression Recognition (MER) is challenging due to multiple possible interpretations of a formula, complicating both parsing and evaluation. In this paper, we introduce the Hierarchical Detail-Focused Recognition dataset (HDR), the first dataset specifically designed to address these issues. It consists of a large-scale training set, HDR-100M, offering an unprecedented scale and diversity with one hundred million training instances. And the test set, HDR-Test, includes multiple interpretations of complex hierarchical formulas for comprehensive model performance evaluation. Additionally, the parsing of complex formulas often suffers from errors in fine-grained details. To address this, we propose the Hierarchical Detail-Focused Recognition Network (HDNet), an innovative framework that incorporates a hierarchical sub-formula module, focusing on the precise handling of formula details, thereby significantly enhancing MER performance. Experimental results demonstrate that HDNet outperforms existing MER models across various datasets.<|reference_end|>
@article{wang2024enhancing,
    title={Enhancing Complex Formula Recognition with Hierarchical Detail-Focused
  Network},
    author={Jiale Wang, Junhui Yu, Huanyong Liu and Chenanran Kong},
    journal={arXiv preprint arXiv:2409.11677},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11677},
    primaryClass={cs.CL}
}