HiPS: Hierarchical PDF Segmentation of Textbooks

The growing demand for effective tools to parse PDF-formatted texts, particularly structured documents such as textbooks, reveals the limitations of current methods developed mainly for research paper segmentation. This work addresses the challenge of hierarchical segmentation in complex structured documents, with a focus on legal textbooks that contain layered knowledge essential for interpreting and applying legal norms. We examine a Table of Contents (TOC)-based technique and approaches that rely on open-source structural parsing tools or Large Language Models (LLMs) operating without explicit TOC input. To enhance parsing accuracy, we incorporate preprocessing strategies such as OCR-based title detection, XML-derived features, and contextual text features. These strategies are evaluated based on their ability to identify section titles, allocate hierarchy levels, and determine section boundaries. Our findings show that combining LLMs with structure-aware preprocessing substantially reduces false positives and improves extraction quality. We also find that when the metadata quality of headings in the PDF is high, TOC-based techniques perform particularly well. All code and data are publicly available to support replication. We conclude with a comparative evaluation of the methods, outlining their respective strengths and limitations.