Precise Table Recognition by Making Use of Reference Tables

  • Claudia Wenzel
  • Wolfgang Tersteegen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1655)

Abstract

The ScanTab system represents a knowledge-based approach to table recognition in scanned documents. In contrast to most systems which recognize tables by grouping layout information, our system uses predefined information about which table types may appear in the documents. This enables a very accurate detection able to cope with distorted tables and tables providing little layout information, e.g., no lines, bad alignment, or few rows. Table recognition starts with the detection of the table header. Afterwards, this header is compared with table headers of known reference tables. Having determined the correct reference table, the information kept in the knowledge base is utilized to compute the complete table structure. A graphical user interface allows an easy and fast specification of reference tables.

Reference

  1. 1.
    O. Hori, D. S. Dörmann: Robust Table-form Structure Analysis Based on Box-Driven Reasoning. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 95)Google Scholar
  2. 2.
    S. Chandran, R. Kasturi: Structural Recognition of Tabulated Data. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR93)Google Scholar
  3. 3.
    E. Green, M. Krishnamoorthy: Model-Based Analysis of Printed Tables. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 95)Google Scholar
  4. 4.
    Y. Hirayama: A Method For Table Structure Analysis Using DP Matching. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 95)Google Scholar
  5. 5.
    J. Shamilian, H. Baird, T. Wood: A Retargetable Table Reader. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 97)Google Scholar
  6. 6.
    S. Baumann, M. Ben Hadj Ali, A. Dengel, T. Jäger, M. Malburg, A. Weigel, C. Wenzel: Message Extraction from Printed Documents-A Complete Solution. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 97)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Claudia Wenzel
    • 1
  • Wolfgang Tersteegen
    • 2
  1. 1.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany
  2. 2.University for Applied Sciences of Emden (FHO)EmdenGermany

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