Improving Handwritten Mathematical Expression Recognition via Integrating Convolutional Neural Network With Transformer and Diffusion-Based Data Augmentation

Handwritten mathematical expression recognition (HMER) poses a formidable challenge due to the intricate two-dimensional structures and diverse handwriting styles. This paper introduces a novel approach to improve HMER accuracy by employing an integrated, high-capacity architecture that combines Tra...

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Autori principali:	Yibo Zhang, Gaoxu Li
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2024-01-01
Serie:	IEEE Access
Soggetti:	CNN data augmentation denoising diffusion probabilistic model DDPM handwritten mathematical expression recognition HMER
Accesso online:	https://ieeexplore.ieee.org/document/10529259/
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