JACIII Vol.21 No.4 pp. 697-708
doi: 10.20965/jaciii.2017.p0697


Calligraphy-Stroke Learning Support System Using Projector and Motion Sensor

Takafumi Matsumaru and Masashi Narita

Graduate School of Information, Production and Systems, Waseda University
2-7 Hibikino, Wakamatsu-ku, Katakyushu, Fukuoka 808-0135, Japan

November 29, 2016
January 27, 2017
July 20, 2017
calligraphy-stroke learning support system, brushwork teaching, trajectory generation, handwriting expression, projection

This paper presents a newly developed calligraphy-stroke learning support system. The system incorporates the following functions: a) Displaying brushwork, trajectory, and handwriting; b) recording and playback of an expert’s calligraphy-stroke; and c) teaching a learner a calligraphy-stroke. The following features of the system demonstrate the contributions of our study. (1) The system, which consists of a sensor and projector, is simple and compact, so as to be easily introduced to the usual educational fields and practical leaning situations. (2) Three-dimensional calligraphy-stroke is instructed by presenting two-dimensional visual information. (3) A trajectory region is generated in the form of continuous squares, calculated using a brush model based on the brush position information measured by a sensor. (4) Handwriting is expressed by mapping a handwriting texture image according to ink concentration and the brush handling state. The results of the trial experiment suggest the effectiveness of the learning support function in terms of letter form and calligraphy-stroke.

Cite this article as:
T. Matsumaru and M. Narita, “Calligraphy-Stroke Learning Support System Using Projector and Motion Sensor,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.4, pp. 697-708, 2017.
Data files:
  1. [1] M. Narita and T. Matsumaru, “Calligraphy-stroke learning support system using projector,” Proc. 24th IEEE Int. Symp. on Robot and Human Interactive Communication (IEEE RO-MAN 2015), pp. 640-645, DOI: 10.1109/ROMAN.2015.7333576, 2015.
  2. [2] N. Muranaka, T. Yamamoto, and S. Imanishi, “A Calligraphy Mastering Support System Using Virtual Reality Technology and its Learning Effects,” Trans. Institute of Electrical Engineers of Japan, A, Vol.123, No.12, pp. 1206-1216, 2003 (in Japanese).
  3. [3] H. Nishino, K. Murayama, K. Shuto, T. Kagawa, and K. Utsumiya, “A Calligraphy Training System Based on Skill Acquisition Through Haptization,” J. of Ambient Intelligence and Humanized Computing, Vol.2, Issue 4, pp. 271-284, DOI: 10.1007/s12652-010-0042-y, 2011.
  4. [4] C. L. Teo, E. Burdet, and H. P. Lim, “A robotic teacher of Chinese handwriting,” Proc. 10th Symp. on Haptic Interfaces for Virtual Environment and Teleoperator Systems 2002 (HAPTICS 2002), pp. 335-341, DOI: 10.1109/HAPTIC.2002.998977, 2002.
  5. [5] M. Eid, M. Mansour, A. E. Saddik, and R. Iglesias, “A haptic multimedia handwriting learning system,” Proc. Int. Workshop on Educational Multimedia and Multimedia Education 2007 (Emme ’07), pp. 103-108, DOI: 10.1145/1290144.1290161, Sep. 2007.
  6. [6] N. Tsuda, N. Kato, and Y. Nomura, “Instruction of Arm Motion for Calligraphy Using Vibrotactile Stimulations,” Proc. of 2011 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM2011), pp. 677-682, DOI: 10.1109/AIM.2011.6026981, 2011.
  7. [7] N. Tsuda, M. Narita, Y. Nomura, and N. Kato, “Development of Pressure Presentation Device for Low-key Correction of Writing Motion,” Trans. of Human Interface Society, Vol.15, No.3, pp. 219-226, 2013 (in Japanese).
  8. [8] H. T. F. Wong and H. H. S. Ip, “Virtual brush: a model-based synthesis of Chinese calligraphy,” Computers & Graphics, Vol.24, Issue 1, pp. 99-113, DOI: 10.1016/S0097-8493(99)00141-7, 2000.
  9. [9] Y. Suzuki, Y. Inoguchi, and S. Horiguch, “Brush Model for Calligraphy Using a Haptic Device,” Trans. of the Virtual Reality Society of Japan, Vol.10, No.4, pp. 573-580, 2005 (in Japanese).
  10. [10] Y. Ishibashi and T. Asano, “Media Synchronization Control with Prediction in a Remote Haptic Calligraphy System,” Proc. of 4th Int. Conf. on Advances in Computer Entertainment Technology (ACE2007), pp. 79-86, 2007.
  11. [11] C.-W. Liang, M.-C. Hsieh, and K.-Y. Young, “A VR-based Calligraphy Writing System with Force Reflection,” Int. J. of Automation and Smart Technology (auSMT), Vol.1, No.2, pp. 83-91, DOI: 10.5875/ausmt.v1i2.90, 2011.
  12. [12] N. S. H. Chu and C.-L. Tai, “Real-Time Painting with an Expressive Virtual Chinese Brush,” IEEE Computer Graphics and Applications, Vol.24, No.5, pp. 76-85, DOI: 10.1109/MCG.2004.37, 2004.
  13. [13] W. Baxter, Y. Liu, and M. C. Lin, “A viscous paint model for interactive applications,” Computer Animation and Virtual Worlds, Vol.15, Issue 3-4, pp. 433-441, DOI: 10.1002/cav.47, 2004.
  14. [14] K. Miura, A. Matsui, and S. Katsura, “Synthesis of Motion-Reproduction Systems Based on Motion-Copying System Considering Control Stiffness,” IEEE/ASME Trans. on Mechatronics, Vol.21, Issue 2, pp. 1015-1023, DOI: 10.1109/TMECH.2015.2478897, 2016.
  15. [15] H. Shimada, Y. Shimada, and M. Ohkura, “A Virtual Calligraphy System Drawable with a Chinese Brush,” Trans. of Information Processing Society of Japan, Vol.47, No.12, pp. 3392-3401, 2006 (in Japanese).
  16. [16] J. Shin and M. Marumoto, “Ink Diffusion Simulation for 3D Virtual Calligraphym” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.17, No.4, pp. 598-603, DOI: 10.20965/jaciii.2013.p0598, 2013.
  17. [17] H. Miyashita and H. Uchihira, “Calligraphy Expressions Based on Electronic Musical Instruments Metaphor,” AIDIA (Asia Interior Design Institute Association) J., Vol.14, pp. 66-73, 2014.
  18. [18] M. Otsuki, K. Sugihara, A. Kimura, F. Shibata, and H. Tamura, “MAI Painting Brush: An interactive device that realizes the feeling of real painting,” Proc. of 23th Annual ACM Symp. on User Interface Software and Technology (UIST ’10), pp. 97-100, 2010.
  19. [19] J.-S. Yeh, T.-Y. Lien, and M. Ouhyoung, “On the effects of haptic display in brush and ink simulation for Chinese painting and calligraphy,” Proc. of 10th Pacific Conf. on Computer Graphics and Applications 2002, pp. 439-441, DOI: 10.1109/PCCGA.2002.1167892, 2002.
  20. [20] M. Miura and T. Toda, “Estimating Writing Neatness from Online Handwritten Data,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.6, pp. 946-952, DOI: 10.20965/jaciii.2014.p0946, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024