In this paper, we present a generic approach for reliable identification of the table of content (TOC) pages in scanned documents. We use multiple sources of information to obtain a reliable assessment of the TOC pages and the position of articles. These sources are produced by using three methods: title matching, section keyword matching, and numeric content. Finally a combination component is used to score potential TOC pages and select the best candidates. The system is used to identify the table of content, locate the beginning of articles, aid the process of advertisement identification (where present), and in general, identify the structure of scanned documents for the process of article extraction and online deployment of digital content. Results of applying the algorithms to an 80-years archive of Time weekly magazine are presented.