Analysis of book documents' table of content based on clustering

Abstract

Table of contents (TOC) recognition has attracted a great deal of attention in recent years. After reviewing the merits and drawbacks of the existing TOC recognition methods, we have observed that book documents are multi-page documents with intrinsic local format consistency. Based on this finding we introduce an automatic TOC analysis method through clustering. This method first detects the decorative elements in TOC pages. Then it learns a layout model used in the TOC pages through clustering. Finally, it generates TOC entries and extracts their hierarchical structure under the guidance of the model. More specifically, broken lines are taken into account in the method. Experimental results show that this method achieves high accuracy and efficiency. In addition, this method has been successfully applied in a commercial E-book production software package.

The Labeled Segmentation of Printed Books

“…The most direct originates in work that grows out of the INEX and ICDAR book structure extraction com- petitions (Kazai et al, , 2010Doucet et al, 2011Doucet et al, , 2013, in which participants are challenged to recognize the fine-grained structure present in documents (recognizing, for example, that the current article has sections entitled "Abstract," "Introduction," "Data, "References," etc.). The most successful systems recognize structure by parsing the table of contents Meunier, 2005, 2009;Wu et al, 2013;Gao et al, 2009) rather than relying on the content of the book itself. Our work primarily differs in the fundamental design choice of prescribing a fix set of categories into which we classify pages (in order to enable comparison across documents) rather than prioritizing the idiosynractic structure of a book (which is useful for generating new tables of contents).…”

“…While other work has focused on extracting the idiosyncratic structure inherent in each book, such as recognizing chapter boundaries in order to automatically generate a table of contents, or link a parsed table of contents to positions in a book Meunier, 2005, 2009;Wu et al, 2013;Gao et al, 2009), labeling document segments with a fixed typology has complementary benefits, allowing researchers to identify consistent categories in all books regardless of the names assigned by a specific author or publisher, or popular at a given time. 1 At the same time, book structure labeling presents real challenges to automatic identification.…”

Searching online book documents and analyzing book citations

“…Accurate extraction of the table of content is a challenging task for book retrieval systems. ToC recognition was previously studied to enable inside book search and navigation [7,21]. However, they assume entries at the same level in a ToC share consistent features and each entry can be matched to the related title in the body part.…”

OCR-Free Table of Contents Detection in Urdu Books

“…There has been an increase in interest in document analysis community to develop efficient algorithms for detecting ToC pages in a document [1]. The research related to ToC analysis can be divided into three areas [2]: ToC page detection, ToC parsing and to link the actual pages with these recognized parts.…”

“…Belaïd [6] proposed a Part-of-Speech tagging (PoS) based algorithm for ToC detection. Gao et al [2] proposed a ToC parsing and decorative elements detection technique based on clustering. Mandal et al [7] used the spatial distribution properties of contents for detection of ToC pages.…”