An Electronic Secure Voting System Based on Automatic Paper Ballot Reading

  • Iñaki Goirizelaia
  • Koldo Espinosa
  • Jose Luis Martin
  • Jesus Lázaro
  • Jagoba Arias
  • Juan J. Igarza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

A secret and secure ballot is the core of every democracy. We all feel proud of being able to decide the future of our countries by making appropriate use of our right to vote in an election. However, how can we improve the efficiency of the voting process? Democratic governments should have mechanisms which ensure the integrity, security and privacy of its citizens at the polls during an election process. This paper describes a new electronic secure voting system, based on automatic paper ballot reading, which can be utilized to offer efficient help to officials and party representatives during elections. It presents how the system is organized, it also describes our OCR system and how it is implemented to read paper ballots, and it ends showing some experimental results.

References

  1. 1.
    Cranor, L.F., Cytron, R.K.: Sensus: A security-Conscious Electronic Polling System for the Internet. In: Proceedings of the Hawaii International Conference on Systems Sciences, Wailea Hawaii USA, January 7-10 (1997)Google Scholar
  2. 2.
    California Internet Voting Task Force. “A report on the feasibility of Internet voting”. California Secretary of State Bill Jones (January 2000), http://www.ss.ca.gov/executive/ivote/final_report.htm
  3. 3.
    Bao-Chang, P., Si-Chang, W., Guang-Yi, Y.: A Method of Recognising Handprinted Characters. Computer Recognition and Human Production of Handwriting, World Scientific Publ. Co., 37–60 (1989)Google Scholar
  4. 4.
    Voting. “What is What could be”. Calthech MIT Voting Technology Project (July 2001), http://web.mit.edu/voting/
  5. 5.
    Lee, E.W., Chae, S.I.: Fast design of reduced-complexity nearest neighbor classifiers using triangular inequality. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 562–566 (Mayo 1998)Google Scholar
  6. 6.
    González, R.C., Woods, R.E.: Digital Image processing. Addison-Wesley/Ediciones Díaz de Santos (1996)Google Scholar
  7. 7.
    Oh, I.S., Lee, J.S., Suen, C.Y.: Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 1089–1094 (October 1999)Google Scholar
  8. 8.
    Gdalyahu, Y., Weinshall, D.: Flexible Syntactic Matching of Curves and Its Application to Automatic Hierarchical Classification of Silhouettes. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 1312–1327 (December 1999)Google Scholar
  9. 9.
    Phillips, I.T., Chhabra, A.K.: Empirical Performance Evaluation of Graphics Recognition Systems. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 849–870 (September 1999)Google Scholar
  10. 10.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 226–239 (March 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Iñaki Goirizelaia
    • 1
  • Koldo Espinosa
    • 1
  • Jose Luis Martin
    • 1
  • Jesus Lázaro
    • 1
  • Jagoba Arias
    • 1
  • Juan J. Igarza
    • 1
  1. 1.School of EngineeringUniversity of the Basque CountryBilbaoSpain

Personalised recommendations