CHITRA: Cognitive handprinted input-trained recursively analyzing system for recognition of alphanumeric characters

  • Belur V. Dasarathy
  • K. P. Bharath Kumar
Article

Abstract

A novel system for recognition of handprinted alphanumeric characters has been developed and tested. The system can be employed for recognition of either the alphabet or the numeral by contextually switching on to the corresponding branch of the recognition algorithm. The two major components of the system are the multistage feature extractor and the decision logic tree-type catagorizer. The importance of “good” features over sophistication in the classification procedures was recognized, and the feature extractor is designed to extract features based on a variety of topological, morphological and similar properties. An information feedback path is provided between the decision logic and the feature extractor units to facilitate an interleaved or recursive mode of operation. This ensures that only those features essential to the recognition of a particular sample are extracted each time. Test implementation has demonstrated the reliability of the system in recognizing a variety of handprinted alphanumeric characters with close to 100% accuracy.

Key words

Character recognition handprinted alphanumerics decision tree logic multistage interleaved feature extraction-categorization approach 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. T. Tou and R. C. Gonzalez, “Recognition of handwritten characters by topological feature extraction and multi-level categorization,”IEEE Trans. Comput. C-21:776–785 (July 1972).Google Scholar
  2. 2.
    S. H. Unger, “Pattern recognition and detection,”Proc. IEE 47:1737–1752 (October 1959).Google Scholar
  3. 3.
    J. R. Parks, “A Multi-level System of Analysis for Mixed Font and Hand Block Printed Character Recognition,” inAutomatic Interpretation and Classification of Images, A. Grasselli, ed. (Academic Press, New York, 1969), pp. 295–322.Google Scholar
  4. 4.
    G. H. Granlund, “Fourier processing for handprint character recognition,”IEEE Trans. Comput. C-21:201–204 (February 1972).Google Scholar
  5. 5.
    D. Koxlay, “Feature Extraction in a Learning Optical Character Recognition Machine,” Symposium of Feature Extraction and Selection in Pattern Recognition at Argonne National Laboratory (October 1970).Google Scholar
  6. 6.
    G. Tauschek, “Reading Machine,” U. S. Pat. 2026329 (December 1935).Google Scholar
  7. 7.
    R. B. Hennis, “IBM 1975 optical page reader, part I-system design,”IBM J. Res. Dev. 12(5):346–353 (September 1968).Google Scholar
  8. 8.
    U. Niesser and P. Weene, “A note on human recognition of handprinted characters,”Inf. Control 3:191–196 (June 1960).Google Scholar
  9. 9.
    G. Nagy, “State of art in pattern recognition,”Proc. IEEE 56:836–862 (May 1968).Google Scholar
  10. 10.
    K. Fukunaga,Introduction to Statistical Pattern Recognition (Academic Press, New York, 1972).Google Scholar
  11. 11.
    J. S. Bomba, “Alphanumeric character recognition using local operations,”Proc. EJCC (1959).Google Scholar
  12. 12.
    F. C. Greanias et al., “The recognition of handwritten numerals by contour analysis,”IBM J. Res. Dev. 7:14–21 (January 1963).Google Scholar
  13. 13.
    I. H. Sublette and J. Tults, “Character recognition by digital feature extraction,”RCA Rev. 23:60–79 (March 1962).Google Scholar
  14. 14.
    H. Genchiet al., “Recognition of handprinted numerals for automatic mail sorting,”Proc. IEEE 56:1292–1301 (August 1968).Google Scholar
  15. 15.
    P. W. Weeks, “Rotating raster character recognition system,”AIEE Trans. SO, Pt. I, 353–359 (September 1961).Google Scholar
  16. 16.
    J. R. Singer, “A self organizing recognition system,”Proc. WJCC (1961).Google Scholar
  17. 17.
    A. B. S. Hussainet al., “Results on Munson's data,”IEEE Trans. Comput. C-21:201–204 (February 1972).Google Scholar
  18. 18.
    R. J. Spinrad, “Machine recognition of handprinting,”Inf. Control 8:124–142 (April 1965).Google Scholar
  19. 19.
    M. Beun, “A flexible method for automatic reading of handwritten numerals,” Phillips Technical Review, Vol.33, 1973, pp. 89–101.Google Scholar
  20. 20.
    R. Narasimhan, “Syntax directed interpretation of classes of pictures,”Commun. ACM 9:166–173 (March 1966).Google Scholar
  21. 21.
    M. Eden, “Handwriting and pattern recognition,”IRE Trans. Inf. Theory 8:160–172 (February 1962).Google Scholar
  22. 22.
    A. C. Shaw, “Parsing of graph representable pictures,”J. ACM 17:453–481 (July 1970).Google Scholar
  23. 23.
    R. Narasimhan, “A syntax-aided recognition scheme for handprinted English letters,”Pattern Recognition 3:345–361 (1971).Google Scholar
  24. 24.
    B. V. Dasarathy, “An integrated nonparametric sequential approach to multi-class pattern classification,”Int. J. Syst. Sci. 4:449–460 (1973).Google Scholar
  25. 25.
    K. P. Bharath Kumar, “An Innovative System for Recognition of Hand Printed Characters,” Master's thesis, Indian Institute of Science, School of Automation, Bangalore, India (1974).Google Scholar

Copyright information

© Plenum Publishing Corporation 1978

Authors and Affiliations

  • Belur V. Dasarathy
    • 1
  • K. P. Bharath Kumar
    • 2
  1. 1.M & S Computing, Inc.Huntsville
  2. 2.Department of Electrical EngineeringUniversity of HawaiiHonolulu

Personalised recommendations