A Hybrid Vision Transformer Approach for Mathematical Expression Recognition

• URL: http://arxiv.org/abs/2603.07929v1
• Date: Mon, 09 Mar 2026 03:49:57 GMT
• Title: A Hybrid Vision Transformer Approach for Mathematical Expression Recognition
• Abstract summary: 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.<n>A coverage attention decoder is used to better track attention's history to handle the under-parsing and over-parsing problems.
• Score: 0.23090185577016445
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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