Advertisement

Application of Three-Level Handprinted Documents Recognition in Medical Information Systems

  • Jerzy Sas
  • Marek Kurzynski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

In this paper the application of novel three-level recognition concept to processing of some structured documents (forms) in medical information systems is presented. The recognition process is decomposed into three levels: character recognition, word recognition and form contents recognition. On the word and form contents level the probabilistic lexicons are available. The decision on the word level is performed using results of character classification based on a character image analysis and probabilistic lexicon treated as a special kind of soft classifier. The novel approach to combining these both classifiers is proposed, where fusion procedure interleaves soft outcomes of both classifiers so as to obtain the best recognition quality. Similar approach is applied on the semantic level with combining soft outcomes of word classifier and probabilistic form lexicon. Proposed algorithms were experimentally applied in medical information system and results of automatic classification of laboratory test order forms obtained on the real data are described.

Keywords

Word Recognition Character Recognition Hospital Information System Semantic Level Support Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, C., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques. Pattern Recognition 36, 2271–2285 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Lu, Y., Gader, P., Tan, C.: Combination of Multiple Classifiers Using Probabilistic Dictionary and its Application to Postcode Generation. Pattern Recognition 35, 2823–2832 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Kuncheva, L.: Combining Classifiers: Soft Computing Solutions. In: Pal, S., Pal, A. (eds.) Pattern Recognition: from Classical to Modern Approaches, pp. 427–451. World Scientific, Singapore (2001)CrossRefGoogle Scholar
  4. 4.
    Kuncheva, L.I.: Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets and Systems 122, 401–407 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Sas, J., Kurzynski, M.: Multilevel Recognition of Structured Handprinted Documents – Probabilistic Approach. In: Kurzynski, M., Puchala, E. (eds.) Computer Recognition Systems, Proc. IV Int. Conference, pp. 723–730. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Sas, J., Kurzynski, M.: Application of Statistic Properties of Letter Succession in Polish Language to Handprint Recognition. In: Kurzynski, M. (ed.) Computer Recognition Systems, Proc. IV Int. Conference, pp. 731–738. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Sas, J.: Handwritten Laboratory Test Order Form Recognition Module for Distributed Clinic. J. of Medical Informatics and Technologies 8, 59–68 (2004)Google Scholar
  8. 8.
    Kurzynski, M., Sas, J.: Combining Character Level Classifier and Probabilistic Lexicons in Handprinted Word Recognition – Comparative Analysis of Methods. In: Proc. XI Int. Conference on Computer Analysis and Image Processing. LNCS, Springer, Heidelberg (2005) (to appear)Google Scholar
  9. 9.
    Devroye, L., Gyorfi, P., Lugossi, G.: A Probabilistic Theory of Pattern Recognition. Springer, New York (1996)zbMATHGoogle Scholar
  10. 10.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, Chichester (2001)zbMATHGoogle Scholar
  11. 11.
    Vinciarelli, A., et al.: Offline Recognition of Unconstrained Handwritten Text Using HMMs and Statistical Language Models. IEEE Trans. on PAMI 26, 709–720 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jerzy Sas
    • 1
  • Marek Kurzynski
    • 2
  1. 1.Institute of Applied InformaticsWroclaw University of TechnologyWroclawPoland
  2. 2.Faculty of Electronics, Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations