PDF parsers have recently improved on page-level layout understanding. However, recovering a document-global section hierarchy with reliable boundaries remains brittle for deeply structured books: many systems expose only page-local heading roles, assume shallow depth, or rely on high-quality PDF tags or Table of Contents (TOC) metadata, and public gold-standard data for deep book hierarchies is scarce. We present HiPS for hierarchical PDF segmentation of doctrinal legal books and make two main contributions. First, we release a gold-standard benchmark of 49 open-access law books with 9,812 manually curated headings, hierarchy levels, and page anchors, enabling evaluation of title detection, hierarchy reconstruction, and section boundary assignment. Second, we introduce complementary segmentation pipelines: a TOC-based parser for books with reliable outline metadata and a TOC-free LLM-refined pipeline that combines OCR whitespace cues, XML typography, and local context. Across a broad comparison against open-source parsers and multimodal/LLM baselines, the TOC-based pipeline is strongest when metadata is complete, while the LLM-refined pipeline improves heading precision, deep-level recovery, and boundary quality when metadata is missing or noisy.

翻译：PDF解析器近年来在页面级布局理解方面取得了进展。然而，对于深度结构化的书籍，恢复具有可靠边界的文档全局章节层次结构仍然存在困难：许多系统仅暴露页面级标题角色、假设浅层深度或依赖高质量的PDF标签或目录元数据，且用于深层书籍层次结构的公开黄金标准数据稀缺。我们提出HiPS用于教义法学书籍的层次化PDF分割，并做出两项主要贡献。首先，我们发布了一个包含49本开放获取法律书籍的黄金标准基准数据集，其中包含9,812个手动整理标题、层次级别和页面锚点，从而支持标题检测、层次结构重建和章节边界分配评估。其次，我们引入了互补的分割流水线：适用于具有可靠元数据书籍的基于目录解析器，以及结合OCR空白线索、XML排版和局部上下文的无目录大语言模型精炼流水线。在与开源解析器及多模态/大语言模型基线的广泛比较中，当元数据完整时基于目录的流水线表现最佳，而当元数据缺失或存在噪声时，大语言模型精炼流水线在标题精确度、深层恢复和边界质量方面有所提升。