Enhancing document structure analysis using visual analytics

During the last decade national archives, libraries, museums 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 systems. 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 available as well-structured XML-files) and is therefore not used in the search process. In this paper, a new approach for analyzing 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, adaptation and re-training for a different document type is not possible. Our proposed method allows an efficient and effective adaptation of the structure analysis process by combining 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.