Symbolic Data Structure for Postal Address Representation and Address Validation Through Symbolic Knowledge Base

  • P. Nagabhushan
  • S. A. Angadi
  • B. S. Anami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


The postal address data and the domain information for address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the postal address and the information relevant for validating the postal address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of postal address validation checks the different components of the postal address for consistency before using it for further processing. In the present work a symbolic knowledge base supported address validation system is developed and tested for about 500 addresses. The system efficiency is observed to be 95.6% in validating the addresses automatically.


Postal Address validation Symbolic object knowledge base Frames 


  1. 1.
    Kubota, K., Egami, K.: Technology trend of postal automation, NEC Research and Development. Spl Issue on Postal Technology 40(2), 127–136 (1999)Google Scholar
  2. 2.
    Garibotto, G.: Computer Vision in Postal Automation. Elsag Bailey- TELEROBOT (2002)Google Scholar
  3. 3.
    Nagabhushan, P.: Towards Automation in Indian Postal Services: A Loud Thinking. Technovision, Spl Volume, 128–139 (1998)Google Scholar
  4. 4.
    Kasturi, R., O’Gorman, L., Govindaraju, V.: Document Image Analysis: A Primer. Sadhana Part 1 27, 3–22 (2002)CrossRefGoogle Scholar
  5. 5.
    Premalatha, M.R., Nagabhushan, P.: An algorithmic prototype for automatic verification and validation of PIN code: A step towards Postal Automation. In: NCDAR-2001, July 13th and 14th, pp. 225–233 (2001)Google Scholar
  6. 6.
    Nagamani, M.R., Nagabhushan, P.: Knowledge based approach to Determine the Destination Postal Code Through Address Block Extraction: A case study towards Postal Automation. In: NCDAR-2003 held at PESCE, Mandya, July 11th and 12th, pp. 152–163 (2003)Google Scholar
  7. 7.
    Setlur, S., Lawson, A., Govindaraju, V., Srihari, S.N.: Truthing, Testing and Evaluation Issues in Complex Systems. In: Sixth IAPR International Conference on Document Analysis and Recognition, Seattle, WA, pp. 1205–1214 (2001) Google Scholar
  8. 8.
    Universal Postal Union Address Standard, FGDC Address Standard Version 2Google Scholar
  9. 9.
    Nagabhushan, P., Angadi, S.A., Anami, B.S.: A Knowledge base supported Inferencing of Address Components in Postal Mail. In: NVGIP-2005, Shimoga, March 2nd and 3rd (2005)Google Scholar
  10. 10.
    Lecture Notes of short term course on symbolic and fuzzy approaches to data analysis, April 21-26 (1997)Google Scholar
  11. 11.
    Nagabhushan, P., Angadi, S.A., Anami, B.S.: A Knowledge based Fast PIN code Validation System for Dispatch Sorting of Postal Mail. In: International Conference on Cognitive systems, New Delhi, December 14th and 15th (2004)Google Scholar
  12. 12.
    Diday, E.: Knowledge Discovery from the Symbolic Data and the SODAS Software. In: PKDD 2000 workshop on Symbolic data Analysis, Lyon, September 12th (2000)Google Scholar
  13. 13.
    Bock, H.-H., Diday, E.: Analysis of symbolic Data, Heidelberg (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • P. Nagabhushan
    • 1
  • S. A. Angadi
    • 1
    • 2
  • B. S. Anami
    • 3
  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysore
  2. 2.Department of Computer ApplicationsBECBagalkot
  3. 3.Department of Computer Science & EngineeringBECBagalkot

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