Enhancing Document Structure Analysis using Visual Analytics

During the last decade national archives, libraries, muse-ums and companies started to make their records, books and files electronically available. In order to allow efficient access of this information, the content of the documents must be stored in database and information retrieval sys-tems. State-of-the-art indexing techniques mostly rely on the information explicitly available in the text portions of documents. Documents usually contain a significant amount of implicit information such as their logical structure which is not directly accessible (unless the documents are avail-able as well-structured XML-files) and is therefore not used in the search process. In this paper, a new approach for an-alyzing the logical structure of text documents is presented. The problem of state-of-the-art methods is that they have been developed for a particular type of documents and can only handle documents of that type. In most cases, adap-tation and re-training for a different document type is not possible. Our proposed method allows an efficient and effec-tive adaptation of the structure analysis process by combin-ing state-of-the-art machine learning with novel interactive visualization techniques, allowing a quick adaptation of the structure analysis process to unknown document classes and new tasks without requiring a predefined training set