Abstract
A powerful and flexible technique to identify, classify and process documents using images from a scanning process is presented. The types of documents can be described to the system as a set of differentiating features in a case base using shape trees. The features are filtered and abstracted from an extremely reduced scanner image of the document. Classification rules are stored with the cases to enable precise recognition and further mark reading and Optical Character Recognition (OCR) process. The method is implemented in a system which actually processes the majority of requests for medical lab procedures in Germany. A large practical experiment with data from practitioners was performed. An average of 97% of the forms were correctly identified; none were identified incorrectly. This meets the quality requirements for most medical applications. The modular description of the recognition process allows for a flexible adaptation of future changes to the form and content of the document’s structures.
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References
Abmayr, W. (1994). Einführung in die digitale Bildverarbeitung. Stuttgart: Teubner.
Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603–619.
Epshtein, B., & Ullman, S. (2005). Feature hierarchies for object classification. In Proc. Inter- national Conference on Computer Vision, 30(1), pp. 220–227.
Ferrari, V., Fevrier, L., Jurie, F., & Schmid, C. (2008). Groups of adjacent contour segments for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1), 36–51.
Huang, M., DeMenthon, D., Doermann, D., Golebiowski, L., & Hamilton, B.A. (2005). Document ranking by layout relevance. In Eighth International Conference on Document Analysis and Recognition.
Leibe, B. (2004). Interleaved object categorization and segmentation. PhD thesis, ETH Zürich.
Leibe, B., & Schiele, B. (2003). Interleaved object categorization and segmentation. In Proc. British Machine Vision Conference (pp. 759–768).
Lowe, D. (1999). Object recognition from local scale invariant features. In Proc. International Conference on Computer Vision (ICCV).
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision, 60(2), 91–110.
Lunze, T. (2005). Entwurf und Implementierung von Komponenten für Case Base Reasoning zum iSuite Wissensbanksystem. Dresden: Diplomarbeit TU.
Nistér, D., & Stewénius, H. (2006). Scalable recognition with a vocabulary tree. In Proc. IEEE Int. Conference on Computer Vision and Pattern Recognition (CVPR).
Persoon, E., & Fu, K. (1977). Shape discrimination using Fourier descriptors.IEEE Transactions on Systems, Man, and Cybernetics, SMC-7(3), 1119–1122.
Rosenfeld, A. (2006). Digital picture processing, computer science and applied mathematics (Vol. 2). New York: Academic.
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Henker, U., Petersohn, U., Ultsch, A. (2009). The Precise and Efficient Identification of Medical Order Forms Using Shape Trees. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_60
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DOI: https://doi.org/10.1007/978-3-642-01044-6_60
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