A Novel Approach for Detection of Alteration in Ball Pen Writings

  • Rajesh Kumar
  • Nikhil R. Pal
  • J. D. Sharma
  • Bhabatosh Chanda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


Addition or alteration to documents that have profound implication is very common. The technique that Forensic Document Examiners (FDEs) use for the examination of such documents is basically a physical examination. In this paper we consider the alteration detection as a two-class pattern recognition problem. Image processing techniques are used for feature extraction and a neural network based feature analysis technique is used for finding a set of discriminatory features. The results using a nearest neighbor classifier are very encouraging. The results also demonstrate the effectiveness of feature analysis.


Alteration ball pen feature analysis image processing 


  1. 1.
    Osborne, A.S.: Questioned Documents. Boyd Printing Co., New York (1929)Google Scholar
  2. 2.
    Ellen, D.: The Scientific Examination of Documents Methods and Techniques, 2nd edn. Taylor and Francis, London (2003)Google Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Printice Hall, New Jersey (1996)Google Scholar
  4. 4.
    Koschan, A., Abidi, M.: Digital Color Image Processing. Wiley Interscience, New Jersey (2008)CrossRefGoogle Scholar
  5. 5.
    Berens, J., Finlayson, G.D., Qiu, G.: A Statistical Image of Color Space. In: IEE Proc. International conference on Image Processing and its Application (1), pp. 348–352 (1999)Google Scholar
  6. 6.
    Yap, P.T., Parmesaran, R.: Content Based Image Retrieval using Legendre Chromaticity Distribution moments. In: IEE Proc. Visual Image Signal Processing, pp. 17–24 (2006)Google Scholar
  7. 7.
    Teh, C.H., Chin, R.T.: On Image Analysis by the Methods of Moments. IEEE Transaction on Pattern Analysis and Machine Intelligence 10(4), 496–513 (1988)zbMATHCrossRefGoogle Scholar
  8. 8.
    Haralick, R.M., Bosley, R.: Texture Features for Image Classification. In: Proc. Third ERTS symposium, NASA, pp. 1219–1228 (1973)Google Scholar
  9. 9.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman and Hall/CRC, Taylor and Francis (2007)Google Scholar
  10. 10.
    Pal, N.R., Chintalapudi, K.K.: A Connectionist System for Feature Selection. Neural, Parallel and Scientific Computations (5), 359–382 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rajesh Kumar
    • 1
  • Nikhil R. Pal
    • 2
  • J. D. Sharma
    • 3
  • Bhabatosh Chanda
    • 2
  1. 1.Directorate of Forensic ScienceMHA, GOINew DelhiIndia
  2. 2.ECSUIndian Statistical InstituteKolkataIndia
  3. 3.Dr. HSG UniversitySagarIndia

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