GryphOne: Symbol-Aware Masked Diffusion for Structural Refinement in Offline Handwritten Mathematical Expression Recognition
arXiv:2602.03370v2 Announce Type: replace-cross Abstract: Handwritten mathematical expression recognition (HMER) requires reasoning over diverse symbols and structures, yet autoregressive models struggle with exposure bias and syntax inconsistency. We present GryphOne, a discrete diffusion framework which reformulates HMER as iterative symbolic refinement instead of sequential generation. GryphOne progressively refines symbols and relations, removing autoregression and improving consistency. Symbol-aware tokenization and random-masking mutual learning further enhance robustness to handwriting
The continuous evolution of AI models is pushing boundaries in specialized recognition tasks, and the limitations of previous autoregressive methods are now being directly addressed.
This development enhances the accuracy and robustness of AI in interpreting complex, non-sequential inputs like handwritten mathematical expressions, crucial for scientific and educational applications.
The shift from sequential generation to iterative symbolic refinement in HMER fundamentally alters how AI tackles handwriting recognition challenges.
- · AI researchers in HMER
- · Educational technology sector
- · Scientific computing platforms
- · Any industry relying on transcribing complex handwritten notes
- · Autoregressive HMER model developers
- · Traditional OCR solutions
- · Manual data entry services
Improved accuracy in digitizing complex handwritten coursework and research, reducing manual effort.
Accelerated development in fields reliant on mathematical notation, such as physics and engineering, due to more efficient data processing.
Potential for new human-computer interfaces that seamlessly integrate handwritten input for complex problem-solving and ideation.
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Read at arXiv cs.LG