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arXiv · 2603.07929

A Hybrid Vision Transformer Approach for Mathematical Expression Recognition

Anh Duy Le·Van Linh Pham·Vinh Loi Ly·Nam Quan Nguyen·Huu Thang Nguyen·Tuan Anh Tran
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Abstract

One of the crucial challenges taken in document analysis is mathematical expression recognition. Unlike text recognition which only focuses on one-dimensional structure images, mathematical expression recognition is a much more complicated problem because of its two-dimensional structure and different symbol size. In this paper, we propose using a Hybrid Vision Transformer (HVT) with 2D positional encoding as the encoder to extract the complex relationship between symbols from the image. A coverage attention decoder is used to better track attention's history to handle the under-parsing and over-parsing problems. We also showed the benefit of using the [CLS] token of ViT as the initial embedding of the decoder. Experiments performed on the IM2LATEX-100K dataset have shown the effectiveness of our method by achieving a BLEU score of 89.94 and outperforming current state-of-the-art methods.

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Anh Duy Le, Van Linh Pham, Vinh Loi Ly, Nam Quan Nguyen, Huu Thang Nguyen, Tuan Anh Tran. 2026-03-09. A Hybrid Vision Transformer Approach for Mathematical Expression Recognition. https://doi.org/10.1109/dicta56598.2022.10034626

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