Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network
This work addresses the problem of accurate formula recognition for applications in education and scientific computing, representing an incremental improvement with a new dataset and method.
The paper tackles the challenge of recognizing complex mathematical expressions by introducing a new dataset (HDR-100M with 100 million training instances) and a novel network (HDNet), which significantly enhances performance over existing models.
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.