Symbol-Aware Reasoning with Masked Discrete Diffusion for Handwritten Mathematical Expression Recognition

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Autori principali:	Kawakatsu, Takaya, Ishiyama, Ryo
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
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_version_	1866911418382548992
author	Kawakatsu, Takaya Ishiyama, Ryo
author_facet	Kawakatsu, Takaya Ishiyama, Ryo
contents	Handwritten Mathematical Expression Recognition (HMER) requires reasoning over diverse symbols and 2D structural layouts, yet autoregressive models struggle with exposure bias and syntactic inconsistency. We present a discrete diffusion framework that reformulates HMER as iterative symbolic refinement instead of sequential generation. Through multi-step remasking, the proposal progressively refines both symbols and structural relations, removing causal dependencies and improving structural consistency. A symbol-aware tokenization and Random-Masking Mutual Learning further enhance syntactic alignment and robustness to handwriting diversity. On the MathWriting benchmark, the proposal achieves 5.56\% CER and 60.42\% EM, outperforming strong Transformer and commercial baselines. Consistent gains on CROHME 2014--2023 demonstrate that discrete diffusion provides a new paradigm for structure-aware visual recognition beyond generative modeling.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_03370
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Symbol-Aware Reasoning with Masked Discrete Diffusion for Handwritten Mathematical Expression Recognition Kawakatsu, Takaya Ishiyama, Ryo Computer Vision and Pattern Recognition Machine Learning Handwritten Mathematical Expression Recognition (HMER) requires reasoning over diverse symbols and 2D structural layouts, yet autoregressive models struggle with exposure bias and syntactic inconsistency. We present a discrete diffusion framework that reformulates HMER as iterative symbolic refinement instead of sequential generation. Through multi-step remasking, the proposal progressively refines both symbols and structural relations, removing causal dependencies and improving structural consistency. A symbol-aware tokenization and Random-Masking Mutual Learning further enhance syntactic alignment and robustness to handwriting diversity. On the MathWriting benchmark, the proposal achieves 5.56\% CER and 60.42\% EM, outperforming strong Transformer and commercial baselines. Consistent gains on CROHME 2014--2023 demonstrate that discrete diffusion provides a new paradigm for structure-aware visual recognition beyond generative modeling.
title	Symbol-Aware Reasoning with Masked Discrete Diffusion for Handwritten Mathematical Expression Recognition
topic	Computer Vision and Pattern Recognition Machine Learning
url	https://arxiv.org/abs/2602.03370

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