Document Reading Order Recovery is a fundamental task in document image understanding, playing a pivotal role in enhancing Retrieval-Augmented Generation (RAG) and serving as a critical preprocessing step for large language models (LLMs). Existing methods often struggle with complex layouts(e.g., multi-column newspapers), high-overhead interactions between cross-modal elements (visual regions and textual semantics), and a lack of robust evaluation benchmarks. We introduce XY-Cut++, an advanced layout ordering method that integrates pre-mask processing, multi-granularity segmentation, and cross-modal matching to address these challenges. Our method significantly enhances layout ordering accuracy compared to traditional XY-Cut techniques. Specifically, XY-Cut++ achieves state-of-the-art performance (98.8 BLEU overall) while maintaining simplicity and efficiency. It outperforms existing baselines by up to 24\% and demonstrates consistent accuracy across simple and complex layouts on the newly introduced DocBench-100 dataset. This advancement establishes a reliable foundation for document structure recovery, setting a new standard for layout ordering tasks and facilitating more effective RAG and LLM preprocessing.

翻译：文档阅读顺序恢复是文档图像理解的一项基础任务，在增强检索增强生成（RAG）方面发挥着关键作用，并作为大型语言模型（LLMs）的关键预处理步骤。现有方法通常在处理复杂版面（例如多栏报纸）、跨模态元素（视觉区域与文本语义）之间的高开销交互以及缺乏鲁棒的评估基准方面存在困难。我们提出了XY-Cut++，一种集成了预掩码处理、多粒度分割和跨模态匹配的高级版面排序方法，以应对这些挑战。与传统XY-Cut技术相比，我们的方法显著提升了版面排序的准确性。具体而言，XY-Cut++在保持简洁高效的同时，实现了最先进的性能（总体BLEU分数达98.8）。它在新引入的DocBench-100数据集上，相较于现有基线方法性能提升高达24%，并在简单和复杂版面上均表现出一致的准确性。这一进展为文档结构恢复奠定了可靠基础，为版面排序任务树立了新标准，并促进了更有效的RAG和LLM预处理。