Skip to main content
SpringerLink
Log in

An interactive grading and learning system for chinese calligraphy

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Chinese calligraphy is an oriental art. In this paper, an interactive calligraphic guiding system is first proposed to grade the score of written characters by using the image processing and the fuzzy inference techniques. The written documents are automatically segmented. Three quantized features, the center, the size and the projections of each written character, are extracted to measure the score of calligraphy. The system also gives some improving instructions for users. Some experimental results are given to show the validity and effectiveness of our proposed system. Through this useful system, users could learn and practice Chinese calligraphy at home.

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. Guo B. (1995). Gate to Chinese Calligraphy. Foreign Languages Press, Beijing

    Google Scholar 

  2. Chen, Y.: Practicing your chinese calligraphy. http://www.resources.emb.gov.hk/chical/ (2006)

  3. Tang, L., Kender, J.: A off-line stroke-based handwritten word segmentation and recognition method for low-quality educational videos. In: Proceedings of the 16th International Symposium on Multimedia Software Engineering pp. 565–568 (2004)

  4. Han C.C., Tseng Y.L., Fan K.C., Wang A.B. (1995). Coarse classification of chinese characters via stroke clustering method. Pattern Recogn. Lett. 16(10): 1079–1089

    Article  Google Scholar 

  5. Wen M.-G., Han C.-C., Fan K.-C., Tang D.-W. (2003). A coarse classification scheme on printed chinese characters by encoding the feature points. J. Inf. Sci. Eng. 19: 555–570

    Google Scholar 

  6. Wen M.-G., Fan K.-C., Han C.-C. (2004). Classification of chinese characters using pseudo skeleton features. J. Inf. Sci. Eng. 20: 903–922

    Google Scholar 

  7. Ko, M.-A., Kim, Y.-M.: A simple ocr method from strong perspective view. In: Proceedings of the 33th Workshop on Applied Imagery Pattern Recognition 235–240 (2004)

  8. Wang A.B., Fan K.C., Wu W.H. (1998). Speeding up chinese character recognition in an automatic document reading system. Pattern Recogn. 31: 1601–1612

    Article  Google Scholar 

  9. Wang A.B., Fan K.C. (2001). Optical recognition of handwritten chinese characters by hierarchical radical matching method. J. Pattern Recog. 34: 15–35

    Article  MATH  Google Scholar 

  10. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley (1992)

  11. Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions System, Man Cybern. 9: 62–66

    Article  Google Scholar 

  12. Kittler J., Illingworth J. (1986). Minimum error thresholding. Pattern Recogn. 19: 41–47

    Article  Google Scholar 

  13. Cho S., Haralick R.M., Yi S. (1989). Improvement of kittler and illingworth’s minimum error thresholding. Pattern Recogn. 22: 609–617

    Article  Google Scholar 

  14. Sahoo P.K. (1988). A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41: 233–260

    Article  Google Scholar 

  15. Lin C., Lee C.G. (1996). Neural Fuzzy Systems. Prentice-Hall, New Jersey

    Google Scholar 

  16. Jang J., Sun C., Mizutani E. (1997). Neuro-Fuzzy and Soft Computing. Prentice-Hall, New Jersey

    Google Scholar 

  17. Homnan B., Benjapolakul W. (2004). Application of fuzzy inference to CDMA soft handoff in mobile communication systems. Fuzzy Sets Systems 144: 345–363

    Article  MathSciNet  Google Scholar 

  18. Hong L., Sun J. (2006). Bifurcations of forced oscillators with fuzzy uncertainties by the generalized cell mapping method. Chaos Solitons Fractals 27: 895–904

    Article  MATH  MathSciNet  Google Scholar 

  19. Lo S. (2006). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mater. Process. Technol. 142: 665–675

    Article  Google Scholar 

  20. Yilmaz O., Eyercioglu O., Gindy N. (2006). A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. J. Mater. Process. Technol. 172: 363–371

    Article  Google Scholar 

  21. Mari M., Dellepiane S. (1997). A non-linear image processing approach through fuzzy measures. Pattern Recogn. Lett. 18: 1109–1115

    Article  Google Scholar 

  22. Flaig A., Barner K.E., Arce G.R. (2000). Fuzzy ranking: theory and applications. Signal Process. 80: 1017–1036

    Article  MATH  Google Scholar 

  23. Kim M.H., Jang D.S., Yang Y.K. (2001). A robust-invariant pattern recognition model using fuzzy ART. Pattern Recogn 34: 1685–1696

    Article  MATH  Google Scholar 

  24. Banerjee M., Kundu M. (2003). Edge based features for content based image retrieval. Pattern Recogn. 36: 2649–2661

    Article  Google Scholar 

  25. Li C., Huang J., Chen C. (2004). Soft computing approach to feature extraction. Fuzzy Sets Systems 147: 119–140

    Article  MATH  MathSciNet  Google Scholar 

  26. Yen J., Langari R. (1999). Fuzzy Logic: Intelligence, Control and Information. Prentice Hall, New Jersey

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National United University, Miaoli, Taiwan, ROC

    Chin-Chuan Han

  2. Department of Computer Science and Information Engineering, Chung-Hua University, Hsinchu, Taiwan, ROC

    Chih-Hsun Chou & Chung-Shiou Wu

Authors
  1. Chin-Chuan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chih-Hsun Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chung-Shiou Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chin-Chuan Han.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, CC., Chou, CH. & Wu, CS. An interactive grading and learning system for chinese calligraphy. Machine Vision and Applications 19, 43–55 (2008). https://doi.org/10.1007/s00138-007-0076-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-007-0076-0

Keywords

Search

Navigation