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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Liu, C., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques. Pattern Recognition 36, 2271–2285 (2003)
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)
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)
Kuncheva, L.I.: Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets and Systems 122, 401–407 (2001)
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)
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)
Sas, J.: Handwritten Laboratory Test Order Form Recognition Module for Distributed Clinic. J. of Medical Informatics and Technologies 8, 59–68 (2004)
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)
Devroye, L., Gyorfi, P., Lugossi, G.: A Probabilistic Theory of Pattern Recognition. Springer, New York (1996)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, Chichester (2001)
Vinciarelli, A., et al.: Offline Recognition of Unconstrained Handwritten Text Using HMMs and Statistical Language Models. IEEE Trans. on PAMI 26, 709–720 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sas, J., Kurzynski, M. (2005). Application of Three-Level Handprinted Documents Recognition in Medical Information Systems. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_1
Download citation
DOI: https://doi.org/10.1007/11573067_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
Online ISBN: 978-3-540-31658-9
eBook Packages: Computer ScienceComputer Science (R0)