Disentangling Structure and Restoring Hierarchy for Handwritten Mathematical Expression Recognition
Jie Liu, Shucheng Fan, Li Pan
Source abstract
Handwritten mathematical expression recognition aims to recover symbol sequences and hierarchical relations from two-dimensional layouts. Existing methods often rely on linear sequence modeling or local structural composition, making it difficult to jointly capture the main chain and nested dependencies, leading to structural entanglement and unstable parsing. To address this issue, this paper proposes a structural re-indexing and nested decoding network. A structural re-indexing module separates the main chain and hierarchical relations on shared features and uses mutual constraints to model their dependency. An offset field rearranges features into a disentangled representation, reducing relational ambiguity. Based on this, a nested decoder progressively constructs expressions. The main chain provides the global framework, and local structures are expanded at key positions according to relation predictions. Experiments show that the structural re-indexing and nested decoding network achieves higher accuracy, robustness, and structural consistency, especially for complex expressions and long sequences.
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