# Link prediction Graph Neural Networks for structure recognition of Handwritten Mathematical Expressions

arXiv: https://arxiv.org/abs/2511.02288 · HTML: https://mlsanity.com/paper/2511.02288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer](https://mlsanity.com/paper/2105.02412/) (2021)
- [CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.04410/) (2022)
- [ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2405.09032/) (2024)
- [Global Context for improving recognition of Online Handwritten Mathematical Expressions](https://mlsanity.com/paper/2105.10156/) (2021)
- [Local and Global Graph Modeling with Edge-weighted Graph Attention Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2410.18555/) (2024)

## Abstract (arXiv)

We propose a Graph Neural Network (GNN)-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs, where nodes represent symbols and edges capture spatial dependencies. A deep BLSTM network is used for symbol segmentation, recognition, and spatial relation classification, forming an initial primitive graph. A 2D-CFG parser then generates all possible spatial relations, while the GNN-based link prediction model refines the structure by removing unnecessary connections, ultimately forming the Symbol Label Graph. Experimental results demonstrate the effectiveness of our approach, showing promising performance in HME structure recognition.