Automatic semantic header generator for PDF documents

The Concordia INdexing and DIscovery system (CINDI) is an information discovery and retrieval system to enable a reader to discover resources from a bibliographic database. It uses a metadata description called semantic header to describe an information resource, whose content includes title, author name, the subject and sub-subject, etc. Automatic Semantic Header Generator (ASHG) is used to generate a draft version of the semantic header from a resource automatically. The existing system can deal with four special document formats: HTML, TEXT, LATEX, and RTF. Since more and more people use PDF for document exchange, perusal on line or in print format due to PDF document's easy to use and cross platform portability, more documents are published in PDF format. This thesis presents the design and implementation of an extension to the existing ASHG to extract the semantic header from a PDF document automatically. First, the PDF document is converted to plain text file using Xpdf, an open source software. Modification to Xpdf has been made to get better results of the conversion. In order to test the accuracy of the ASHG, 500 articles which are all from computer science field are used in an experiment to generate the semantic header; the results 80% accurate respectively. However the results reveal that the subject classification (about 41%) is the weakest point of ASHG and requiring further work