Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Wargo System: Semi-Automatic Wrapper Generation in Presence of Complex Data Access Modes
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Modeling Documents for Structure Recognition Using Generalized N-Grams
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
AIDAS: Incremental Logical Structure Discovery in PDF Documents
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
PerfectDoc: A Ground Truthing Environment for Complex Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Document Understanding System Using Stochastic Context-Free Grammars
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Interactive wrapper generation with minimal user effort
Proceedings of the 15th international conference on World Wide Web
Logical Structure Analysis of Document Images Based on Emergent Computation
IEICE - Transactions on Information and Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
An efficient pre-processing method to identify logical components from PDF documents
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Journal of the American Society for Information Science and Technology
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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.