将论文页面还原为可编译的LaTeX代码，提升科学文档的自动化处理质量。
TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction
- 构建了面向可编译LaTeX重建的多维度评估基准和大规模训练数据集。
- 20亿参数模型通过强化学习优化，使生成内容在结构与编译上更可靠。
- 适合需要高精度文档数字化的科研人员和出版系统开发者。
现有文档OCR主要针对纯文本或Markdown，忽视了使LaTeX成为科学出版核心的结构与可执行特性。本文研究从科学PDF页面级重建为可编译LaTeX的任务，提出TexOCR-Bench基准和TexOCR-Train大规模训练语料库。TexOCR-Bench包含多维度评估体系，联合评估转录准确率、结构忠实度和端到端编译性。基于TexOCR-Train，我们使用监督微调（SFT）和基于可验证奖励（来自LaTeX单元测试）的强化学习训练了一个20亿参数模型TexOCR，直接强化编译性和引用完整性。在21个前沿模型上的实验表明，现有系统常违反关键文档不变量，包括一致的章节结构、正确的浮动元素位置和有效的标签-引用链接，严重影响编译可靠性与下游可用性。分析显示，采用可验证奖励的强化学习相比SFT显著提升结构与编译性能。
原文摘要 · Abstract (English)
Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing. We study page-level reconstruction of scientific PDFs into compilable LaTeX and introduce TexOCR-Bench, a benchmark, and TexOCR-Train, a large-scale training corpus, for this task. TexOCR-Bench features a multi-dimensional evaluation suite that jointly assesses transcription fidelity, structural faithfulness, and end-to-end compilability. Leveraging TexOCR-Train, we train a 2B-parameter model, TexOCR, using supervised fine-tuning (SFT) and reinforcement learning (RL) with verifiable rewards derived from LaTeX unit tests that directly enforce compilability and referential integrity. Experiments across 21 frontier models on TexOCR-Bench show that existing systems frequently violate key document invariants, including consistent section structure, correct float placement, and valid label-reference links, which undermines compilation reliability and downstream usability. Our analysis further reveals that RL with verifiable rewards yields consistent improvements over SFT alone, particularly on structural and compilation metrics.
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