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A robust model for on-line handwritten japanese text recognition

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Abstract

This paper describes a robust context integration model for on-line handwritten Japanese text recognition. Based on string class probability approximation, the proposed method evaluates the likelihood of candidate segmentation–recognition paths by combining the scores of character recognition, unary and binary geometric features, as well as linguistic context. The path evaluation criterion can flexibly combine the scores of various contexts and is insensitive to the variability in path length, and so, the optimal segmentation path with its string class can be effectively found by Viterbi search. Moreover, the model parameters are estimated by the genetic algorithm so as to optimize the holistic string recognition performance. In experiments on horizontal text lines extracted from the TUAT Kondate database, the proposed method achieves the segmentation rate of 0.9934 that corresponds to a f-measure and the character recognition rate of 92.80%.

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References

  1. Tseng L.Y., Chen R.C.: Segmenting handwritten Chinese characters based on heuristic merging of stroke bounding boxes and dynamic programming. Pattern Recognit. Lett. 19(10), 963–973 (1998)

    Article  Google Scholar 

  2. Zhao S., Chi Z., Shi P., Yan H.: Two-stage segmentation of unconstrained handwritten Chinese characters. Pattern Recognit. 36(1), 145–156 (2003)

    Article  MATH  Google Scholar 

  3. Liang Z., Shi P.: A metasynthetic approach for segmenting handwritten Chinese character strings. Pattern Recognit. Lett. 26(10), 1498–1511 (2005)

    Article  Google Scholar 

  4. Furukawa, N., Tokuno, J., Ikeda, H.: Online character segmentation method for unconstrained handwriting strings using off-stroke features. In: Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, pp. 361–366. La Baule, France (2006)

  5. Liu C.-L., Sako H., Fujisawa H.: Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1395–1407 (2004)

    Article  Google Scholar 

  6. Cheriet M., Kharma N., Liu C.-L., Suen C.Y.: Character Recognition Systems: A Guide for Students and Practitioners. Wiley, New York (2007)

    Book  MATH  Google Scholar 

  7. Su T.-H., Zhang T.-W., Guan D.-J., Huang H.-J.: Off-line recognition of realistic Chinese handwriting using segmentation-free strategy. Pattern Recognit. 42(1), 167–182 (2008)

    Article  Google Scholar 

  8. Murase, H.: Online recognition of free-format Japanese handwritings. In: Proceedings of the 9th International Conference on Pattern Recognition, vol. 2, pp. 1143–1147. Rome, Italy (1988)

  9. Fukushima, T., Nakagawa, M.: On-line writing-box-free recognition of handwritten Japanese text considering character size variations. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp. 359–363. Barcelona, Spain (2000)

  10. Senda, S., Yamada, K.: A maximum-likelihood approach to segmentation-based recognition of unconstrained handwriting text. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, pp. 184–188. Seattle, WA (2001)

  11. Nakagawa M., Zhu B., Onuma M.: A model of on-line handwritten Japanese text recognition free from line direction and writing format constraints. IEICE Trans. Inf. Syst. E 88(D(8), 1815–1822 (2005)

    Article  Google Scholar 

  12. Ding, X., Liu, H.: Segmentation-driven offline handwritten Chinese and Arabic script recognition. In: Proceedings of the Summit on Arabic and Chinese Handwriting, pp. 61–73. College Park, USA (2006)

  13. Zhu, B., Nakagawa, M.: On-line handwritten Japanese text recognition by improving segmentation quality. In: Proceedings of the 11th International Conference on Frontiers in Handwriting Recognition, pp. 379–384. Montréal, Canada (2008)

  14. Tulyakov, S., Govindaraju, V.: Probabilistic model for segmentation based word recognition with lexicon. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, pp. 164–167. Seattle, WA (2001)

  15. Zhou, X.-D., Yu, J.-L., Liu, C.-L., Nagasaki, T., Marukawa, K.: Online handwritten Japanese character string recognition incorporating geometric context, In: Proceedings of the 9th International Conference on Document Analysis and Recognition, pp. 48–52. Curitiba, Brazil (2007)

  16. Chen M.Y., Kundu A., Srihari S.N.: Variable duration hidden Markov model and morphological segmentation for handwritten word recognition. IEEE Trans. Image Process. 4(12), 1675–1687 (1995)

    Article  Google Scholar 

  17. Yu, J.-L., Zhou, X.-D., Liu, C.-L.: Search strategies in online handwritten character string recognition (in Chinese). In: Proceedings of the 1st Chinese Conference on Pattern Recognition, pp. 299–305. Beijing, China (2007)

  18. Zhu B., Nakagawa M.: Segmentation of on-line freely written Japanese text using SVM for improving text recognition. IEICE Trans. Inf. Syst. E E91(D1), 105–113 (2008)

    Article  Google Scholar 

  19. Mori, H., Aso, H.: Postprocessing for Japanese document recognition based and 2nd order Markov model (in Japanese). Technical report of IPS J, NL-94-63, pp. 89–96. (1994)

  20. Platt J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A.J., Bartlett, P., Scholkopf, D., Schuurmanns, D. (eds) Advances in Large Margin Classifiers, MIT Press, Cambridge (1999)

    Google Scholar 

  21. Oda, H., Zhu, B., Tokuno, J., Onuma, M., Kitadai, A., Nakagawa, M.: A compact on-line and off-line combined recognizer. In: Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, pp. 133–138. La Baule, France (2006)

  22. Nakagawa M., Matsumoto K.: Collection of on-line handwritten Japanese character pattern databases and their analysis. Int. J. Document Anal. Recognit. 7(1), 69–81 (2004)

    Google Scholar 

  23. Juang B.-H., Katagiri S.: Discriminative learning for minimum error classification. IEEE Trans. Signal Process. 40(12), 3043–3054 (1992)

    Article  MATH  Google Scholar 

  24. Robbins H., Monro S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Tokyo University of Agriculture and Technology (TUAT), Tokyo, 184-8588, Japan

    Bilan Zhu, Xiang-Dong Zhou & Masaki Nakagawa

  2. National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Cheng-Lin Liu

Authors
  1. Bilan Zhu
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  2. Xiang-Dong Zhou
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  3. Cheng-Lin Liu
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  4. Masaki Nakagawa
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Correspondence to Bilan Zhu.

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Zhu, B., Zhou, XD., Liu, CL. et al. A robust model for on-line handwritten japanese text recognition. IJDAR 13, 121–131 (2010). https://doi.org/10.1007/s10032-009-0111-y

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  • DOI: https://doi.org/10.1007/s10032-009-0111-y

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