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Sub-Stroke-Wise Relative Feature for Online Indic Handwriting Recognition

Published:17 December 2018Publication History
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Abstract

The main problem of Bangla (Bengali) and Devanagari handwriting recognition is the shape similarity of characters. There are only a few pieces of work on writer-independent cursive online Indian text recognition, and the shape similarity problem needs more attention from the researchers. To handle the shape similarity problem of cursive characters of Bangla and Devanagari scripts, in this article, we propose a new category of features called ‘sub-stroke-wise relative feature’ (SRF) which are based on relative information of the constituent parts of the handwritten strokes. Relative information among some of the parts within a character can be a distinctive feature as it scales up small dissimilarities and enhances discrimination among similar-looking shapes. Also, contextual anticipatory phenomena are automatically modeled by this type of feature, as it takes into account the influence of previous and forthcoming strokes. We have tested popular state-of-the-art feature sets as well as proposed SRF using various (up to 20,000-word) lexicons and noticed that SRF significantly outperforms the state-of-the-art feature sets for online Bangla and Devanagari cursive word recognition.

References

  1. H. El Abed, M. Kherallah, V. Märgner, and A. M. Alimi. 2011. On-line Arabic handwriting recognition competition, ADAB database and participating systems. International Journal on Document Analysis and Recognition 14, 1 (2011), 15--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Bharath and S. Madhvanath. 2012. HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 4 (2012), 670--682. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Bhattacharya and U. Pal. 2012. Stroke segmentation and recognition from Bangla online handwritten text. In Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition. 736--741. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. N. Bhattacharya, U. Pal, and F. Kimura. 2013. A system for Bangla online handwritten text. In Proceedings of the 12th International Conference on Document Analysis and Recognition. 1367--1371. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. N. Bhattacharya, U. Pal, and P. P. Roy. 2017. Stroke-order normalisation for online Bangla handwriting recognition. In Proceedings of the 14th International Conference on Document Analysis and Recognition. 206--211.Google ScholarGoogle Scholar
  6. C. Bishop. 2006. Pattern Recognition and Machine Learning. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W. Cho, S. W. Lee, and J. H. Kim. 1995. Modeling and recognition of cursive words with hidden Markov models. Pattern Recognition 28, 12 (Dec. 1995), 1941--1953. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. A. Fink, S. Vajda, U. Bhattacharya, S. K. Parui, and B. B. Chaudhuri. 2010. Online Bangla word recognition using sub-stroke level features and hidden Markov models. In Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition. 393--398. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. V. Frinken, N. Bhattacharya, and U. Pal. 2014. Design of unsupervised feature extraction system for on-line Bangla handwriting recognition. In Proceedings of the 11th IAPR International Workshop on Document Analysis Systems. 355--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. V. Frinken, N. Bhattacharya, S. Uchida, and U. Pal. 2014. Improved BLSTM neural networks for recognition of on-line Bangla complex words. IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition + Structural and Syntactic Pattern Recognition, Lecture Notes in Computer Science, Springer, Berlin, 404--413. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Ghosh and P. P. Roy. 2016. Comparison of zone-features for online Bengali and Devanagari word recognition using HMM. In Proceedings of the 15th International Conference on Frontiers in Handwriting Recognition. 435--440.Google ScholarGoogle Scholar
  12. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. 2009. A novel connectionist system for unconstrained handwriting recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 31, 5 (May 2009), 855--868. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Jaeger, S. Manke, J. Reichert, and A. Waibel. 2001. Online handwriting recognition: The NPen++ recognizer. International Journal on Document Analysis and Recognition 3, 3 (Mar. 2001) 169--180.Google ScholarGoogle ScholarCross RefCross Ref
  14. F. Jelinek. 1994. Statistical Methods for Speech Recognition. MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Katayama, S. Uchida, and H. Sakoe. 2008. A new HMM for on-line character recognition using pen-direction and pen-coordinate features. In Proceedings of the 19th International Conference on Pattern Recognition. 1--4.Google ScholarGoogle Scholar
  16. Y.-F. Lv, L.-L. Huang, D.-H. Wang, and C.-L. Liu. 2013. Learning-based candidate segmentation scoring for real-time recognition of online overlaid Chinese handwriting. In Proceedings of the 12th International Conference on Document Analysis and Recognition. 74--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. R. Plamondon and S. N. Srihari. 2000. On-line and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1 (Jan. 2000), 63--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. L. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2 (Feb. 1989), 257--286.Google ScholarGoogle ScholarCross RefCross Ref
  19. J. Schenk and G. Rigoll. 2006. Novel Hybrid NN/hmm modelling techniques for on-line handwriting recognition. In Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. 619--623.Google ScholarGoogle Scholar
  20. K. Y. Wang, R. G. Casey, and F. M. Wahl. 1982. Document Analysis System. IBM J. Res 26, 647--656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. S. J. Young et al. 1995. The HTK Hidden Markov Model Toolkit Book, Entropic Cambridge Research Laboratory.Google ScholarGoogle Scholar

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  1. Sub-Stroke-Wise Relative Feature for Online Indic Handwriting Recognition

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