A dynamic feature generation system for automated metadata extraction in preservation of digital materials

Obsolescence in storage media and the hardware and software for access and use can render old electronic files inaccessible and unusable. Therefore, the long-term preservation of digital materials has become an active area of research. At the U.S. National Library of Medicine (NLM), we are investigating the preservation of scanned and online medical journal articles, though other data types (e.g., video sequences) are also of interest. Metadata of different types have been proposed to save the information needed to preserve digital materials. Given the ever-increasing volume of medical journals and high labor cost of manual data entry, automated metadata extraction is crucial. A system has been developed at NLM to automatically generate descriptive metadata that includes title, author, affiliation, and abstract from scanned medical journals. A module called ZoneMatch is used to generate geometric and contextual features from a set of issues of each journal. A rule-based labeling module (called ZoneCzar) then uses these features to perform labeling independent of journal layout styles. However, if there are significant style variations among the issues of a same journal, the features generated from one set of journal issues may not be very useful to label a different set. We describe a dynamic feature updating system in which the features used for labeling a current journal issue are generated from previous issues with similar layout style. This new system can adapt to possible style variations among different issues of the same journal. Experimental results presented show that the new system delivers improved labeling performance accuracy.