Non-commutative Logic for Hand-Written Character Modeling

  • Jacqueline Castaing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2385)


We have proposed a structural approach for on-line handwritten character recognition. Models of characters are writer-dependent. They are codified with the help of graphic primitives and represented in a data base. The originality of our approach comes from the ability for the system, to explain and justify its own choice, and to deal with all different writing systems, such as the Latin alphabet, or the Chinese or Japanese scrip for example, providing that an appropriate data base has been built up. For this reason, our recognizer can be very helpful for learners of “exotic” scripts. In this paper, we propose to analyse the recognition process in an appropriate logical framework, given by non-commutative Logic. We first point out the class of sequents which allows us to describe accurately the recognition process in terms of proofs, then, we will give some results about the complexity of the recognition problem depending on the expressive power of the representation language.


Linear Logic Character Recognition Distance Computing Proofs 


Foundations and Complexity of Symbolic Computation Logic and Symbolic Computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abrusci, V.M., Ruet, P.: Non-Commutative Logic I: the Multiplicative Fragment, Annals Pure Appl. Logic, 1998.Google Scholar
  2. 2.
    Brézellec, P., Soldano, H.: ÉLÉNA: A Bottom-Up Learning Method, Proceedings of the Tenth International Conference on Machine Learning, Ahmerst 93, Morgan Kaufmann, pp 9–16.Google Scholar
  3. 3.
    Castaing, J., Brézellec, P.: On-Line Chinese Characters Recognition by means of Symbolic Learning, Proceedings of the International Conference on Chinese Computing’96 June 4–7 Univ. of SingaporGoogle Scholar
  4. 4.
    Castaing, J., Brézellec, P.: Une Méthode Symbolique pour la Reconnaissance de l’écriture Manuelle en Ligne, RFIA 96 RennesGoogle Scholar
  5. 5.
    Chan, K.-F., Yeung, D.-Y.: Elastic Structural Matching for On-Line Handwritten Alphanumeric Character Recognition, Proceedings 14th Int. Conf. Pattern Recognition vol.2, Brisbane, Australia, pp. 1508–1511Google Scholar
  6. 6.
    Connell, S.: On-Line Handwriting Recognition using Multiple Pattern Class Models, submitted to Michigan State University in partial fulfillment of the requirements for the degree Doctor of Pilosophy Department of Computer Science and Engineering 2000Google Scholar
  7. 7.
    Frey, P., Slate, D.: Letter Recognition Using Hollad-Style Adaptive Classifiers, Machine Learning Vol.6 num.2, Kluwer Academic Publishers pp. 161–182Google Scholar
  8. 8.
    Kanovitch, M.I.: Linear Logic as a Logic of Computations, Proceedings 7-th Annual IEEE Symposium on Logic in Computer science, Santa Cruz, pp 200–210, 1992Google Scholar
  9. 9.
    Girard, J.-Y,: Linear Logic, Theoret.Comp. Sci., 50(1):1–102, 1987zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Hu, J., Turin, W.: HMM Based On-Line Handwriting Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.18, no. 10, pp. 1039–1045, Oct. 1996.CrossRefGoogle Scholar
  11. 11.
    Manke, S., Finke, M., Waibel A.: The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System, Neural Information Processing System NIPS 94, pp 1093–1100Google Scholar
  12. 12.
    Nathan, K.S., Bellegarda, J.R., Nahamou, D., Bellegarda, E.J.:On-Line Handwriting Recognition Using Continuous Parameter Hidden Markov Models, Proc. ICASSP’93, vol.5, Minneapolis, MN, pp. 121–124, May 1993.Google Scholar
  13. 13.
    Peterson, J.L,: Computation sequence sets, J.Comput. System Sci.13 1–24 1976zbMATHMathSciNetGoogle Scholar
  14. 14.
    Prevost, L., Milgram, M.: Automatic Allograph Selection and Multiple Classification for Totally UnconstrainedHandwritten Character Recognition, Proceedings 14th Int. Conf. Pattern Recognition vol.2, Brisbane, Australia, pp 381–383Google Scholar
  15. 15.
    Rabiner, L.R: A tutorial on Hidden Markov Models and Selected Application in Speech Recognition, Proceedings of IEEE, 77(2) 1989Google Scholar
  16. 16.
    Rigoll, G., Kosmala, A., Willet, D.: A New Hybrid Approach to Large Vocabulary Cursive handwriting Recognition, Proc. 14th Int. Conf. on Pattern Recognition, Brisbane, Australia, pp. 1512–1514, Aug. 1998.Google Scholar
  17. 17.
    Robinson, J.A.: A Machine Oriented Logic Basedon the Resolution Principle J.ACM 12(1), 23–41Google Scholar
  18. 18.
    Ruet, P.: Non-commutative logic II: sequent calculus and phase semantics, to appear in Mathematical Structure in Computer ScienceGoogle Scholar
  19. 19.
    Scattolin, P., Krzyzak, A.: Weighted Elastic Matching Method for Recognition of Handwritten Numerals in Vision Interface’94, pp178–185, 1994Google Scholar
  20. 20.
    Editor Wang, P.S.P: Characters & Handwriting Recognition: Expanding Frontiers, World Scientific Series 1991Google Scholar
  21. 21.
    Yanikoglu, B.A, Sandon, P.A: Recognizing Off-Line Cursive Handwriting, IEEE Computer Society Conference On Computer Vision and Pattern Recognition, CVPR’94, pp397–403Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jacqueline Castaing
    • 1
  1. 1.LIPN-UMR 7030Galilée UniversityVilletaneuseFrance

Personalised recommendations