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arXiv · 2508.09220

Towards Scalable Training for Handwritten Mathematical Expression Recognition

Haoyang Li·Jiaqing Li·Jialun Cao·Zongyuan Yang·Yongping Xiong
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Abstract

Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical \textbf{E}xpression \textbf{R}ecognition (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 \texttt{Tex80M}, comprising over 80 million high-quality training instances. Then we propose \texttt{TexTeller}, the first HMER model trained at scale, by mix-training \texttt{Tex80M} with a relatively small HME dataset. The expansive training dataset and our refined pipeline have equipped \texttt{TexTeller} 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.

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Haoyang Li, Jiaqing Li, Jialun Cao, Zongyuan Yang, Yongping Xiong. 2025-08-11. Towards Scalable Training for Handwritten Mathematical Expression Recognition. https://arxiv.org/abs/2508.09220

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