Estimating   Writing Neatness from Online   Handwritten Data

Motoki Miura; Takamichi Toda

doi:10.20965/jaciii.2014.p0946

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JACIII Vol.18 No.6 pp. 946-952

doi: 10.20965/jaciii.2014.p0946

(2014)

Paper:

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Estimating Writing Neatness from Online Handwritten Data

Motoki Miura and Takamichi Toda

Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu, Fukuoka 804-8550, Japan

Received:

January 31, 2014

Accepted:

April 15, 2014

Published:

November 20, 2014

Keywords:

consciousness, carefulness, improved handwriting, learning attitudes, anoto digital pen

Abstract

Handwriting is the most fundamental expressive activity in learning. To utilize the intuitiveness and the nature of handwriting, digital pen technology has emerged to capture and transfer notes. We developed AirTransNote, a student note-sharing system that facilitates collaborative and interactive learning in conventional classrooms. A teacher can use the AirTransNote system to share student notes with the class on a projected screen immediately to enhance the group learning experience. However, to improve the effectiveness of sharing notes, the teacher must be able to select an effective note for sharing. This can be difficult and time consuming during a lecture. Moreover, students should be encouraged to improve the presentation of their handwritten notes. Well-written notes are more accessible for other students and reduce irrelevant and careless mistakes. To facilitate learning improvements based on note sharing, we require a method to estimate the neatness of a note automatically. If a method is established, the teacher can easily select effective notes. Furthermore, this method can help provide feedback to the student to improve their writing. We examined 14 basic features from handwritten notes by considering correlation coefficients and found that the variance of pen speed, angular point average, and pen speed average were the significant features for evaluating the neatness of handwritten notes.

Cite this article as:

M. Miura and T. Toda, “Estimating Writing Neatness from Online Handwritten Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.6, pp. 946-952, 2014.

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References

[1] M. Miura, T. Sugihara, and S. Kunifuji, “Improvement of Digital Pen Learning System for Daily Use in Classrooms,” Educational Technology Research, Vol.34, pp. 49-57, October 2011.
[2] A. Gartner, M. C. Kohler, and F. Riessman, “Children teach children: Learning by teaching,” Harper & Row, 1971.
[3] K. Bielaczyc, P. L. Pirolli, and A. L. Brown, “Training in Self-Explanation and Self-Regulation Strategies: Investigating the Effects of Knowledge Acquisition Activities on Problem Solving,” Cognition and Instruction, Vol.13, No.2, pp. 221-252, 1995.
[4] R. Barnard, “Peer tutoring in the Primary Classroom: A Sociocultural Interpretation of Classroom Interaction,” New Zealand J. of Educational Studies, Vol.37, No.1, pp. 57-72, 2002.
[5] P. Y. Simard, D. Steinkraus, and M. Agrawala, “Ink Normalization and Beautification,” Proc. of 2005 Int. Conf. on Document Analysis and Recognition (ICDAR ’05), pp. 1182-1187, August 2005.
[6] L. Julia and C. Faure, “Pattern Recognition and Beautification for a Pen Based Interface,” Proc. of the 3rd Int. Conf. on Document Analysis and Recognition, Vol.1, pp. 58-63, August 1995.
[7] H. Miyao and R. Maruyama, “On-Line Handwritten Flowchart Recognition, Beautification and Editing System,” Int. Conf. on Frontiers in Handwriting Recognition (ICFHR), pp. 83-88, 2012.
[8] B. Paulson and T. Hammond, “PaleoSketch: Accurate Primitive Sketch Recognition and Beautification,” Proc. of the 13th Int. Conf. on Intelligent user interfaces (IUI’08), pp. 1-10, 2008.
[9] X. Zhu and L. Jin, “Calligraphic Beautification of Handwritten Chinese Characters: A Patternized Approach to Handwriting Transfiguration,” Proc. of ICFHR2008, pp. 135-140, 2008.
[10] F. Aşıcıoglŭ and N. Turan, “Handwriting changes under the effect of alcohol,” Forensic Science Int., Vol.132, No.3, pp. 201-210, 2003.
[11] U. Ramer, “An iterative procedure for the polygonal approximation of plane curves,” Computer Graphics and Image Processing, Vol.1, No.3, pp. 244-256, 1972.
[12] M. Miura, S. Akase, and T. Toda, “Estimating the Care with which Notes are Written from Online Handwritten Character Data,” Proc. of the 6th Int. Conf. on Intelligent Interactive Multimedia Systems and Services, pp. 195-203, June 2013.
[13] M. Miura and T. Toda, “Extraction ofWriting Carefulness from Online Handwritten Data,” 22nd FIM Int. Conf. on Interdisciplinary Mathematics, Statistics and Computational Techniques, pp. 113-118, November 2013.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. Miura, T. Sugihara, and S. Kunifuji, “Improvement of Digital Pen Learning System for Daily Use in Classrooms,” Educational Technology Research, Vol.34, pp. 49-57, October 2011.

[2] [2] A. Gartner, M. C. Kohler, and F. Riessman, “Children teach children: Learning by teaching,” Harper & Row, 1971.

[3] [3] K. Bielaczyc, P. L. Pirolli, and A. L. Brown, “Training in Self-Explanation and Self-Regulation Strategies: Investigating the Effects of Knowledge Acquisition Activities on Problem Solving,” Cognition and Instruction, Vol.13, No.2, pp. 221-252, 1995.

[4] [4] R. Barnard, “Peer tutoring in the Primary Classroom: A Sociocultural Interpretation of Classroom Interaction,” New Zealand J. of Educational Studies, Vol.37, No.1, pp. 57-72, 2002.

[5] [5] P. Y. Simard, D. Steinkraus, and M. Agrawala, “Ink Normalization and Beautification,” Proc. of 2005 Int. Conf. on Document Analysis and Recognition (ICDAR ’05), pp. 1182-1187, August 2005.

[6] [6] L. Julia and C. Faure, “Pattern Recognition and Beautification for a Pen Based Interface,” Proc. of the 3rd Int. Conf. on Document Analysis and Recognition, Vol.1, pp. 58-63, August 1995.

[7] [7] H. Miyao and R. Maruyama, “On-Line Handwritten Flowchart Recognition, Beautification and Editing System,” Int. Conf. on Frontiers in Handwriting Recognition (ICFHR), pp. 83-88, 2012.

[8] [8] B. Paulson and T. Hammond, “PaleoSketch: Accurate Primitive Sketch Recognition and Beautification,” Proc. of the 13th Int. Conf. on Intelligent user interfaces (IUI’08), pp. 1-10, 2008.

[9] [9] X. Zhu and L. Jin, “Calligraphic Beautification of Handwritten Chinese Characters: A Patternized Approach to Handwriting Transfiguration,” Proc. of ICFHR2008, pp. 135-140, 2008.

[10] [10] F. Aşıcıoglŭ and N. Turan, “Handwriting changes under the effect of alcohol,” Forensic Science Int., Vol.132, No.3, pp. 201-210, 2003.

[11] [11] U. Ramer, “An iterative procedure for the polygonal approximation of plane curves,” Computer Graphics and Image Processing, Vol.1, No.3, pp. 244-256, 1972.

[12] [12] M. Miura, S. Akase, and T. Toda, “Estimating the Care with which Notes are Written from Online Handwritten Character Data,” Proc. of the 6th Int. Conf. on Intelligent Interactive Multimedia Systems and Services, pp. 195-203, June 2013.

[13] [13] M. Miura and T. Toda, “Extraction ofWriting Carefulness from Online Handwritten Data,” 22nd FIM Int. Conf. on Interdisciplinary Mathematics, Statistics and Computational Techniques, pp. 113-118, November 2013.