# Towards Scalable Training for Handwritten Mathematical Expression Recognition

arXiv: https://arxiv.org/abs/2508.09220 · HTML: https://mlsanity.com/paper/2508.09220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
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- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Image-to-Markup Generation with Coarse-to-Fine Attention](https://mlsanity.com/paper/1609.04938/) (2016)
- [Syntax-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2203.01601/) (2022)
- [UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition](https://mlsanity.com/paper/2404.15254/) (2024)
- [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.11463/) (2022)
- [Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning](https://mlsanity.com/paper/2112.03603/) (2021)
- [PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer](https://mlsanity.com/paper/2407.07764/) (2024)
- [An Empirical Study of Scaling Law for OCR](https://mlsanity.com/paper/2401.00028/) (2023)
- [Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention](https://mlsanity.com/paper/2106.09432/) (2021)
- [SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels](https://mlsanity.com/paper/2502.07172/) (2025)

## Abstract (arXiv)

Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of Handwritten Mathematical Expression Recognition (HMER) has been impeded by the scarcity of data, primarily due to the arduous and costly process of manual annotation. To bridge this gap, we propose a novel method integrating limited handwritten formulas with large-scale LaTeX-rendered formulas by developing a scalable data engine to generate complex and consistent LaTeX sequences. With this engine, we built the largest formula dataset to date, termed textttTex80M, comprising over 80 million high-quality training instances. Then we propose textttTexTeller, the first HMER model trained at scale, by mix-training textttTex80M with a relatively small HME dataset. The expansive training dataset and our refined pipeline have equipped textttTexTeller with state-of-the-art (SOTA) performance across nearly all benchmarks. To advance the field, we will openly release our complete model, entire dataset, and full codebase, enabling further research building upon our contributions.