Journal of Platform Technology

  • 제6권1호
  • /
  • Pages.3-8
  • /
  • 2018
  • /
  • 2289-0181(pISSN)
  • /
  • 2289-019X(eISSN)
ICT플랫폼학회 (ICT Platform Society)
권호 표지

Recognition of Virtual Written Characters Based on Convolutional Neural Network

  • 투고 : 2018.03.02
  • 심사 : 2018.03.30
  • 발행 : 2018.03.31

⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

키워드

과제정보

연구 과제 주관 기관 : Kumoh National Institute of Technology

참고문헌

  1. S. L. Leem, H, S. Jeong, S. Y. Kim, "Remote Drawing Technology Based on Motion Trajectories Analysis", Journal of KIIECT(domestic journal), Vol. 9, No. 2, pp.229-236, 2016.
  2. U.V. Marti, H. Bunke, "Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system", IJPRAI, Vol.15, No.1, pp.65-90, 2001.
  3. R. Kala, H. Vazirani, A. Shukla, R. Tiwari, "Offline handwriting recognition using genetic algorithm", IJCSI, Vol.7, No.2, pp.16-25, 2010.
  4. B. Chacko, V. Krishnan, G. Raju, P. Anto, "Handwritten character recognition using wavelet energy and extreme learning machine", Int. J. Mach. Learn. Cyber, Vol.3, No.2, pp.149-161, 2012. https://doi.org/10.1007/s13042-011-0049-5
  5. A. Graves, M. Liwicki, S. Fernandez, "A novel connectionist system for unconstrained handwriting recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No.5, 2009.
  6. M.K. Brown, W. Turin, J. Hu, "HMM based online handwriting recognition", TPAMI, Vol.18, No.10, pp.1039-1045, 1996. https://doi.org/10.1109/34.541414
  7. X. Li, D. Y., Yeung, "On-line handwritten alphanumeric character recognition using dominant points in strokes", Pattern recognition on 1997, Vol.30, No.1, pp.31-44, 1997.
  8. N. Arica, F. T. Yarman, "An overview of character recognition focused on off-line handwriting", IEEE Transactions on Systems Man and Cybernetics, Vol.31, No.2, pp.216-233, 2001. https://doi.org/10.1109/5326.941845
  9. R. Plamondon, S. N. Srihari, "Online and off-line handwriting recognition: a comprehensive survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.63-84, 2000. https://doi.org/10.1109/34.824821
  10. I.J. Kim, X. Xie, "Handwritten Hangul recognition using deep convolutional neural networks", IJDAR, Vol.18, No.1, pp.1-13, 2015. https://doi.org/10.1007/s10032-014-0229-4
  11. I.J. Kim, C. Choi, S.H. Lee, "Improving discrimination ability of convolutional neural networks by hybrid learning", IJDAR, Vol.19, No.1, pp.1-9, 2016. https://doi.org/10.1007/s10032-015-0256-9
  12. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition" Proc. of the IEEE, Vol. 86, No. 11, pp.2278-2324, 1998. https://doi.org/10.1109/5.726791
  13. N. Srivastava, G. E. Hinton, A. Krizhevsky, "Dropout: a simple way to prevent neural networks from overfitting", JMLR. 2014.
  14. V. Nair and G. Hinton "Rectified linear units improve restricted Boltzmann machines", Proc. of International Conference on Machine Learning, pp. 807-814, 2010.