Repudiation Detection in Handwritten Documents

  • Sachin Gupta
  • Anoop M. Namboodiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Forensic document verification presents a different and interesting set of challenges as opposed to traditional writer identification and verification tasks using natural handwriting. The handwritten data presented to a forensic examiner is often deliberately altered, in addition to being limited in quantity. Specifically, the alterations can be either forged, where one imitates another person’s handwriting; or repudiated, where one deliberately distorts his handwriting in order to avoid identification. In this paper, we present a framework to detect repudiation in forensic documents, where we only have one pair of documents to arrive at a decision. The approach generates a statistically significant confidence score from matching two documents, which can be used to screen the documents that are passed on to an expert examiner. The approach can be extended for detection of forgeries as well.


High Level Feature Reference Document Handwritten Document Forensic Expert False Accept Rate 
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.


  1. 1.
    Morris, R.: Forensic Handwriting Identification: Fundamental concepts and principles. Academic Press, London (2000)Google Scholar
  2. 2.
    Srihari, S., Huang, C., Srinivasan, H., Shah, V.: Biometric and Forensic Aspects of Digital Document Processing. In: Digital Document Processing, pp. 379–405. Springer, London (2007)CrossRefGoogle Scholar
  3. 3.
    Fogarolo, L.: Questioned document examination using graphology (2007),
  4. 4.
    Huber, R., Headrick, A.: Handwriting Identification: Facts and Fundamentals. CRC Press, Boca Roton (1999)Google Scholar
  5. 5.
    Plamondon, R., Lorette, G.: Automatic signature identification and writer verification - the state of the art. Pattern Recognition 22(2), 107–131 (1989)CrossRefGoogle Scholar
  6. 6.
    He, Z., Tang, Y.: Chinese handwriting based writer identification by texture analysis. In: Proceedings of International Conference on Machine Learning and Cybernetics, Shanghai, vol. 6, pp. 3488–3491 (2004)Google Scholar
  7. 7.
    Schomaker, L., Bulacu, M.: Text-independent writer identification and verification using textural and allographic features. IEEE Transactions on Patterh Analysus and Machine Intelligence, Special Issue - Biometrics: Progress and Directions 29(4), 701–717 (2007)Google Scholar
  8. 8.
    Tomai, C., Zhang, B., Srihari, S.: Discriminatory power of handwritten words for writer recognition. In: Proceedings of Int’l conf. of Pattern Recognition, Cambridge, UK, vol. 2, pp. 638–641 (2004)Google Scholar
  9. 9.
    Thumwarin, P., Matsuura, T.: On-line writer recognition for thai based on velocity of barycenter of pen-point movement. In: Proceedings of IEEE Int’l conf. of Image Processing, Singapore, pp. 889–892. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  10. 10.
    Yu, K., Wang, Y., Tan, T.: Writer identification using dynamic features. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 512–518. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Schlapbach, A., Bunke, H.: A writer identification and verification system using hmm based recognizers. Pattern Analysis & Applications 10(1), 33–43 (2007)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Srihari, S., Bandi, K., Beal, M.: A statistical model for writer verification. In: Proceedings of the Int’l conf of Document Analysis and Research, Seoul, Korea, pp. 1105–1109 (2005)Google Scholar
  13. 13.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology. Special Issue on Image- and Video-Based Biometrics 14(1), 4–20 (2004)Google Scholar
  14. 14.
    Jain, A.K., Griess, F., Connell, S.D.: On-line signature verification. Pattern Recognition 35(12), 2963–2972 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recepies in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)Google Scholar
  16. 16.
    Namboodiri, A.M., Gupta, S.: Text independent writer identification from online handwriting. In: Proceedings of Int’l workshop of Frontier in Handwriting Recognition, La Baule, Centre de Congreee Atlantia, France, pp. 23–26 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sachin Gupta
    • 1
  • Anoop M. Namboodiri
    • 1
  1. 1.International Institute of Information Technology, HyderabadIndia

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