Skip to main content
SpringerLink
Log in

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G.A. Carpenter, “Neural network models for pattern recognition and associative memory,” Neural Networks, vol. 2, pp. 243-257, 1989.

    Google Scholar 

  2. G.A. Carpenter, S. Grossberg, and D.B. Rosen, “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,” Neural Networks, vol. 4, pp. 759-771, 1991.

    Google Scholar 

  3. H.D. Chang and J.F. Wang, “Preclassification for handwritten Chinese character recognition by a peripheral shape coding method,” Pattern Recognition, vol. 26, no.5, pp. 711-719, 1993.

    Google Scholar 

  4. H.D. Chang and J.F. Wang, “A robust stroke extraction method for handwritten Chinese characters,” Intl. Journal of Pattern Recognition Artificial Intelligence, vol. 8, no.5, pp. 1223-1239, 1994.

    Google Scholar 

  5. H.D. Chang, J.F. Wang, and S.C. Kuo, “A Bayesian neural network for separating similar complex handwritten Chinese characters,” Pattern Recognition Letters, vol. 15, no.4, pp. 403-408, 1994.

    Google Scholar 

  6. Y.S. Chen and W.H. Hsu, “A modified fast parallel algorithm for thinning digital patterns,” Pattern Recognition Letters, vol. 7, pp. 99-106, 1988.

    Google Scholar 

  7. F.H. Cheng and W.H. Hsu, “Research on Chinese OCR in Taiwan,” Intl. Journal of Pattern Recognition and Artificial Intelligence, vol. 5, nos.1-2, pp. 139-164, 1991.

    Google Scholar 

  8. C.C. Chiang and H.C. Fu, “Using neural nets to recognize handwritten/printed characters,” Proc. Advanced Computer Technology, Reliable Systems and Applications. 5th Annual European Computer Conference CompEuro'91, 1991, pp. 492-496.

  9. Y.A. Dimitriadis and J.L. Coronado, “Towards an art based mathematical editor, that uses on-line handwritten symbol recognition,” Pattern Recognition, vol. 28, no.6, pp. 807-822, 1995.

    Google Scholar 

  10. K. Fukushima and N. Wake, “Handwritten alphanumeric character recognition by the neocognitron,” IEEE Transactions on Neural Networks, vol. 2, no.3, pp. 355-365, 1991.

    Google Scholar 

  11. K.W. Gan and K.T. Lua, “A new approach to stroke and feature point extraction in Chinese character recognition,” Pattern Recognition Letters, vol. 12, pp. 381-387, 1991.

    Google Scholar 

  12. D. Hammerstrom, “Neural networks at work,” IEEE Spectrum, pp. 26-32, June 1993.

  13. T.H. Hildebrand and W. Liu, “Optical recognition of handprinted Chinese characters: Advances since 1980,” Pattern Recognition, vol. 26, no.2, pp. 205-225, 1993.

    Google Scholar 

  14. C.C. Hsieh and H.J. Lee, “A probabilistic stroke-based Viterbi algorithm for handwritten Chinese character recognition,” Intl. Journal of Pattern Recognition and Artificial Intelligence, vol. 7, no.2, pp. 329-352, 1993.

    Google Scholar 

  15. J.S. Huang, T.R. Jang, and F. Chang, “Extracting strokes of a Chinese character based on cross-section sequence graph,” Journal of Information Science and Engineering, vol. 8, pp. 512- 524, 1992.

    Google Scholar 

  16. K.M. Ku and P.P.K. Chiu, “Fast stroke extraction method for handwritten Chinese characters by cross region analysis,” Electronics Letter, vol. 30, no.15, pp. 1210-1212, 1994.

    Google Scholar 

  17. T. Kumamoto et al., “On speeding candidate selection in handprinted Chinese character recognition,” Pattern Recognition, vol. 24, no.8, pp. 793-799, 1991.

    Google Scholar 

  18. H.M. Lee and C.W. Huang, “A fuzzy rule-based system for structure decomposition on handwritten Chinese characters,” in Proc. of IEEE Intl. Conference on Systems, Man, and Cybernetics, vol. 1, USA, October 1994, pp. 487-492.

    Google Scholar 

  19. H.M. Lee and C.S. Lai, “Supervised extended ART: A fast neural network classifier trained by combining supervised and unsupervised learning,” Applied Intelligence, vol. 6, no.2, pp. 117-128, 1996.

    Google Scholar 

  20. T.F. Li and S.S. Yu, “Handprinted Chinese character recognition using the probability distribution feature,” Intl. Journal of Pattern Recognition and Artificial Intelligence, vol. 8, no.5, pp. 1241-1258, 1994.

    Google Scholar 

  21. S.W. Lu and H. Xu, “False stroke detection and elimination for character recognition,” Pattern Recognition Letters, vol. 13, pp. 745-755, 1992.

    Google Scholar 

  22. S. Salzberg, “A nearest hyperrectangle learning method,” Machine Learning, vol. 6, pp. 251-276, 1991.

    Google Scholar 

  23. L.Y. Tseng and C.T. Chuang, “An efficient knowledge-based stroke extraction method for multi-font Chinese characters,” Pattern Recognition, vol. 25, no.12, pp. 1445-1458, 1992.

    Google Scholar 

  24. C.H. Tung and H.J. Lee, “Increasing character recognition accuracy by detection and correction of erroneously identified characters,” Pattern Recognition, vol. 27, no.9, pp. 1259-1266, 1994.

    Google Scholar 

  25. Y.W. Wang, Four-Corner Method, Commercial Press: Taipei, Taiwan, 1967.

    Google Scholar 

  26. R.Y. Wu and W.H. Tsai, “Handprinted Chinese character recognition by neural networks,” International Symposium on Artificial Neural Networks, Taiwan, December 1993, pp. C01-10.

  27. S.L. Xie and M. Suk, “On machine recognition of hand-printed Chinese characters by feature relaxation,” Pattern Recognition, vol. 21, no.1, pp. 1-7, 1988.

    Google Scholar 

  28. T.N. Yang et al., “Hierarchical approach of neural network design for handwritten Chinese character recognition,” International Symposium on Artificial Neural Networks, Taiwan, December 1993, pp. C41-49.

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Hahn-Ming Lee, Chung-Chieh Sheu & Jyh-Ming Chen

Authors
  1. Hahn-Ming Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Chieh Sheu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyh-Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, HM., Sheu, CC. & Chen, JM. Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model. Applied Intelligence 8, 269–285 (1998). https://doi.org/10.1023/A:1008272518100

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008272518100

Search

Navigation