Enhancing Table of Contents Extraction by System Aggregation

Abstract

The OCR-ed books usually lack logical structure information, such as chapters, sections. To enrich the navigation experience of users, several approaches have been proposed to extract table of contents (ToC) from digitised books. In this paper, we introduce an aggregation-based method to enhance ToC extraction using system submissions from the ICDAR Book structure extraction competitions (2009, 2011, and 2013). Our experimental results show that the union of two best approaches outperforms the existing approaches using both the title-based and link-based evaluation measures on a dataset of more than 2000 books. By efficiently combining the results of existing systems in an unsupervised way, we consistently beat the state-of-the-art in book structure extraction, with performance improvements that are statistically significant.