# A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition

arXiv: https://arxiv.org/abs/1712.03991 · HTML: https://mlsanity.com/paper/1712.03991/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [MMHMER:Multi-viewer and Multi-task for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2502.05557/) (2025)
- [SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels](https://mlsanity.com/paper/2502.07172/) (2025)
- [OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios](https://mlsanity.com/paper/2608.18586/) (2026)

## Abstract (arXiv)

In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER). First, the input two-dimensional ink trajectory information of handwritten expression is encoded via the gated recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is also implemented by the GRU-RNN with a coverage-based attention model. The proposed approach can simultaneously accomplish the symbol recognition and structural analysis to output a character sequence in LaTeX format. Validated on the CROHME 2014 competition task, our approach significantly outperforms the state-of-the-art with an expression recognition accuracy of 52.43% by only using the official training dataset. Furthermore, the alignments between the input trajectories of handwritten expressions and the output LaTeX sequences are visualized by the attention mechanism to show the effectiveness of the proposed method.