GryphOne: Symbol-Aware Masked Diffusion for Structural Refinement in Offline Handwritten Mathematical Expression Recognition

Abstract

Handwritten mathematical expression recognition (HMER)requires reasoning over diverse symbols and structures, yet autoregressivemodels struggle with exposure bias and syntax inconsistency. We presentGryphOne, a discrete diffusion framework which reformulates HMER asiterative symbolic refinement instead of sequential generation. GryphOneprogressively refines symbols and relations, removing autoregression andimproving consistency. Symbol-aware tokenization and random-maskingmutual learning further enhance robustness to handwriting diversity. Onthe MathWriting benchmark, GryphOne achieves 5.51% CER and 59.9%EM (ExpRate), outperforming all reimplemented models in the matchedsetting as well as the commercial HMER system. Held-out evaluation onCROHME 2014–2023 further shows strong cross-dataset generalization.