caa a22 4500 467884196 CHVBK 20180406152729.0 cr unu---uuuuu 170328e20060601xx s 000 0 eng 10.1007/s10032-005-0149-4 doi (NATIONALLICENCE)springer-10.1007/s10032-005-0149-4 Detection and analysis of table of contents based on content association [Elektronische Daten] [Xiaofan Lin, Yan Xiong] As a special type of table understanding, the detection and analysis of tables of contents (TOCs) play an important role in the digitization of multi-page documents. Most previous TOC analysis methods only concentrate on the TOC itself without taking into account the other pages in the same document. Besides, they often require manual coding or at least machine learning of document-specific models. This paper introduces a new method to detect and analyze TOCs based on content association. It fully leverages the text information throughout the whole multi-page document and can be directly applied to a wide range of documents without the need to build or learn the models for individual documents. In addition, the associations of general text and page numbers are combined to make the TOC analysis more accurate. Natural language processing and layout analysis are integrated to improve the TOC functional tagging. The applications of the proposed method in a large-scale digital library project are also discussed. Springer-Verlag, 2005 Table of contents nationallicence Document structure analysis nationallicence Table recognition nationallicence Optical character recognition nationallicence Algorithm combination nationallicence Lin Xiaofan Hewlett-Packard Laboratories, 1501 Page Mill Road, MS 1203, 94304, Palo Alto, CA, USA aut Xiong Yan KLA-Tencor Corporation, One Technology Drive, MS 1-2242, 95035, Milpitas, CA, USA aut International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/2-3(2006-06-01), 132-143 1433-2833 8:2-3<132 2006 8 10032 https://doi.org/10.1007/s10032-005-0149-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-005-0149-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lin Xiaofan Hewlett-Packard Laboratories, 1501 Page Mill Road, MS 1203, 94304, Palo Alto, CA, USA aut NATIONALLICENCE 700 1- Xiong Yan KLA-Tencor Corporation, One Technology Drive, MS 1-2242, 95035, Milpitas, CA, USA aut NATIONALLICENCE 773 0- International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/2-3(2006-06-01), 132-143 1433-2833 8:2-3<132 2006 8 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer