# Global Context for improving recognition of Online Handwritten Mathematical Expressions

arXiv: https://arxiv.org/abs/2105.10156 · HTML: https://mlsanity.com/paper/2105.10156/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 21 May 2021.

## Built on by (most cited first, 3 of 3)

- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [A Transformer-based Math Language Model for Handwritten Math Expression Recognition](https://mlsanity.com/paper/2108.05002/) (2021)
- [Link prediction Graph Neural Networks for structure recognition of Handwritten Mathematical Expressions](https://mlsanity.com/paper/2511.02288/) (2025)

## Abstract (arXiv)

This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). The classification model is trained by multiple paths of symbols and spatial relations derived from the Symbol Relation Tree (SRT) representation of HMEs. The method benefits from global context of a deep bidirectional Long Short-term Memory network, which learns the temporal classification directly from online handwriting by the Connectionist Temporal Classification loss. To recognize an online HME, a symbol-level parse tree with Context-Free Grammar is constructed, where symbols and spatial relations are obtained from the temporal classification results. We show the effectiveness of the proposed method on the two latest CROHME datasets.