Journal of Information Processing Systems

  • 제8권4호
  • /
  • Pages.653-668
  • /
  • 2012
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Online Recognition of Handwritten Korean and English Characters

  • Ma, Ming (Dept. of Game Engineering, Paichai University) ;
  • Park, Dong-Won (Dept. of Game Engineering, Paichai University) ;
  • Kim, Soo Kyun (Dept. of Game Engineering, Paichai University) ;
  • An, Syungog (Dept. of Game Engineering, Paichai University)
  • 투고 : 2012.05.15
  • 심사 : 2012.05.21
  • 발행 : 2012.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a post-processing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system.

키워드

참고문헌

  1. Brown, P. (1987).The Acoustic Modeling Problem in Automatic Speech Recognition. PhD thesis, Comp. Sci. Dep., Carnegie Mellon Univ.
  2. Jaeger, S., Manke, S., Reichert, J., & Waibel, A. (2001). Online handwriting recognition: the NPen++ recognizer. International Journal on Document Analysis and Recognition 3 (3), 169-180. https://doi.org/10.1007/PL00013559
  3. Jung, K., & Kim, H. J. (2000). On-line recognition of cursive Korean characters using graph representation. Pattern Recognition 33, 399-412. https://doi.org/10.1016/S0031-3203(99)00062-X
  4. Kim, H. Y., & Kim, J. H. (2001). Hierarchical random graph representation of handwritten character and its application to Hangul recognition. Pattern Recognition 34 (2), 187-201. https://doi.org/10.1016/S0031-3203(99)00222-8
  5. Kim, P. K., & Kim, H. J. (1995). On-line recognition of run-on Korean characters. Proc. 3rd IEEE Int. Conf. on Document Analysis and Recognition '95, Vol.1, Montreal, Canada, pp.54-57.
  6. Kim, S. K., Kim, J. W., & Kim, H. J. (1996) .On-line recognition of cursive Korean characters using neural networks. Neurocomputing 10(3): 291-305 https://doi.org/10.1016/0925-2312(96)00128-2
  7. Kwon, J. O., Sin, B. K., & Kim, J. H. (1997). Recognition of On-line Cursive Korean Characters Combining Statistical and Structural Models. Pattern Recognition, 30, 8, pp.1255-1263 https://doi.org/10.1016/S0031-3203(96)00164-1
  8. Kwon, O. S., & Kwon, Y. B. (1993). An on-line recognition of Hangul handwriting using dynamic generation of line segments. Proceeding of The Twentieth KISS Spring Conference, pp.151-154.
  9. Liu C. L., Jaeger, S., & Nakagawa, M. (2004). Online recognition of Chinese characters: the state-ofthe- art. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.2.
  10. Park, H. S., & Lee, S. W. (1996). Off-line recognition of large-set handwritten characters with multiple hidden Markov models, Pattern Recognition 29(2), 231-244. https://doi.org/10.1016/0031-3203(95)00081-X
  11. Parisse, C. (1996). Global word shape processing in on-line recognition of handwriting. IEEE Trans. Pattern Anal. Mach. Intell, 18 (4) 460-464. https://doi.org/10.1109/34.491629
  12. Plamondon, R., & Srihari, S. N. (2000). On-line and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence , 22 (1), 63-84. https://doi.org/10.1109/34.824821
  13. Rabiner, L. R., & Levinson, S. E. (1985). A speaker-independent, syntax-directed, connected word recognition system based on hidden Markov models and level building. IEEE Trans. Acousf. Speech Signal Process., 33, (3), pp.561-573. https://doi.org/10.1109/TASSP.1985.1164586
  14. Senior, A. W., & Robinson, A. J. (1998). An on-line cursive handwriting recognition system, IEEE Trans. Pattern Anal. Mach. Intell, 20 (3) 309-321. https://doi.org/10.1109/34.667887
  15. Vuori, V. (2002). Adaptive Methods for On-Line Recognition of Isolated Handwritten Characters, PHD dissertation
  16. Yacoubi, A. E., Gilloux, M., Sabourin, R., & Suen, C. (1999). An HMM-based approach for on-line unconstrained handwritten word modeling and recognition. IEEE Trans. Pattern Anal. Mach. Intel, 21 (8) 752-760. https://doi.org/10.1109/34.784288

피인용 문헌

  1. Towards robust and reliable multimedia analysis through semantic integration of services vol.75, pp.22, 2016, https://doi.org/10.1007/s11042-014-2445-9
  2. Graphemes Segmentation for Arabic Online Handwriting Modeling vol.10, pp.4, 2014, https://doi.org/10.3745/JIPS.02.0006
  3. Remote Drawing Technology Based on Motion Trajectories Analysis vol.9, pp.2, 2016, https://doi.org/10.17661/jkiiect.2016.9.2.229
  4. Improving clustering performance using independent component analysis and unsupervised feature learning vol.8, pp.1, 2018, https://doi.org/10.1186/s13673-018-0148-3