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Classification of Elementary Stamp Shapes by Means of Reduced Point Distance Histogram Representation

  • Paweł Forczmański
  • Dariusz Frejlichowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7376)

Abstract

The paper presents a problem of stamp shape classification, where an input stamp is given as a bitmap containing binary values. While every stamp features a specific geometrical form coming from the de facto standards of stamping process, thus it can be classified as round, oval, square, rectangular or triangular. We assume to have a detected stamp and in this paper we deal with the stage of features extraction and reduction, by means of Point Distance Histogram (at the stage of features extraction) and Principal Component Analysis and Linear Discriminant Analysis (at the stage of dimensionality reduction). The final classification employs similarity evaluation involving hand-drawn templates, ideal shapes and average descriptors calculated for the entire database. Despite the fact that there are only several basic stamp shapes, the task is not trivial since there are many variations in size, silhouette and complexity of individual stamps. It should be emphasized that the scanned document may be degraded in quality thus extracted stamp can be distorted (the silhouette may be discontinuous and/or can be noised). The paper provides some experimental results on real documents with different types of stamps and a comparison with a classical Discrete Cosine Transform (DCT) and PCA applied on image matrix.

Keywords

Dimensionality Reduction Linear Discriminant Analysis Discrete Cosine Transform Recognition Rate Average Descriptor 
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.

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References

  1. 1.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)zbMATHCrossRefGoogle Scholar
  2. 2.
    Ueda, K., Nakamura, Y.: Automatic verification of seal impression patterns. In: Proc. 7th. Int. Conf. on Pattern Recognition, pp. 1019–1021 (1984)Google Scholar
  3. 3.
    Pham, T.D.: Unconstrained logo detection in document images. Pattern Recognition 36, 3023–3025 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Zhu, G., Jaeger, S., Doermann, D.: A robust stamp detection framework on degraded documents. In: Proceedings — SPIE The International Society For Optical Engineering, vol. 6067 (2006)Google Scholar
  5. 5.
    Zhu, G., Doermann, D.: Automatic Document Logo Detection. In: The 9th International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 864–868 (2007)Google Scholar
  6. 6.
    He, J., Downton, A.C.: Configurable Text Stamp Identification Tool with Application of Fuzzy Logic. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 201–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Frejlichowski, D., Forczmański, P.: General Shape Analysis Applied to Stamps Retrieval from Scanned Documents. In: Dicheva, D., Dochev, D. (eds.) AIMSA 2010. LNCS, vol. 6304, pp. 251–260. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Forczmański, P., Frejlichowski, D.: Robust Stamps Detection and Classification by Means of General Shape Analysis. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 360–367. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Wood, J.: Invariant Pattern Recognition: A Review. Pattern Recognition 29, 1–17 (1996)CrossRefGoogle Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer, NY (1986)Google Scholar
  11. 11.
    Kukharev, G., Forczmański, P.: Data Dimensionality Reduction for Face Recognition. Machine Graphics & Vision 13(1/2), 99–122 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Forczmański
    • 1
  • Dariusz Frejlichowski
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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