$A^2R^2$: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement

• URL: http://arxiv.org/abs/2507.20890v1
• Date: Mon, 28 Jul 2025 14:41:57 GMT
• Title: $A^2R^2$: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement
• Authors: Zhecheng Li, Guoxian Song, Yiwei Wang, Zhen Xiong, Junsong Yuan, Yujun Cai,
• Abstract summary: Vision-language models (VLMs) have demonstrated strong performance across a variety of visual understanding tasks.<n>We propose $A2R2$: Advancing Img2La Conversion via Visual Reasoning with Attention-Guided Refinement.<n>For effective evaluation, we introduce a new dataset, Img2La-TexHard-1K, consisting of 1,100 carefully curated and challenging examples.
• Score: 48.856390495568114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Img2LaTeX is a practically significant task that involves converting mathematical expressions or tabular data from images into LaTeX code. In recent years, vision-language models (VLMs) have demonstrated strong performance across a variety of visual understanding tasks, owing to their generalization capabilities. While some studies have explored the use of VLMs for the Img2LaTeX task, their performance often falls short of expectations. Empirically, VLMs sometimes struggle with fine-grained visual elements, leading to inaccurate LaTeX predictions. To address this challenge, we propose $A^2R^2$: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement, a framework that effectively integrates attention localization and iterative refinement within a visual reasoning framework, enabling VLMs to perform self-correction and progressively improve prediction quality. For effective evaluation, we introduce a new dataset, Img2LaTex-Hard-1K, consisting of 1,100 carefully curated and challenging examples designed to rigorously evaluate the capabilities of VLMs within this task domain. Extensive experimental results demonstrate that: (1) $A^2R^2$ significantly improves model performance across six evaluation metrics spanning both textual and visual levels, consistently outperforming other baseline methods; (2) Increasing the number of inference rounds yields notable performance gains, underscoring the potential of $A^2R^2$ in test-time scaling scenarios; (3) Ablation studies and human evaluations validate the practical effectiveness of our approach, as well as the strong synergy among its core components during inference.

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