173
Views
13
CrossRef citations to date
0
Altmetric
Research Review

Novel approaches towards slope and slant correction for tri-script handwritten word images

, , , , &
Pages 159-170 | Received 20 Sep 2018, Accepted 21 Jan 2019, Published online: 08 Feb 2019

References

  • Plamondon R, Srihari SN. Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell. 2000;22(1):63–84. doi: 10.1109/34.824821
  • Mori S, Suen CY, Yamamoto K. Historical review of OCR research and development. Proc IEEE. 1992;80(7):1029–1058. doi: 10.1109/5.156468
  • Farooq F, Govindaraju V, Perrone M. Pre-processing methods for handwritten Arabic documents. In: Eighth International Conference on Document Analysis and Recognition, Proceedings, August. IEEE; 2005. p. 267–271.
  • Das Gupta J, Chanda B. 2014. An efficient slope and slant correction technique for off-line handwritten text word. International Conference of Emerging Applications of Information Technology, Indian Statistical Institute, Kolkata.
  • Bera SK, Kar R, Saha S, et al. A one-pass approach for slope and slant estimation of tri-script handwritten words. J Intell Syst. 2017. doi: 10.1515/jisys-2018-0105
  • Kavallieratou E, Dromazou N, Fakotakis N, et al. An integrated system for handwritten document image processing. Int J Pattern Recognit Artif Intell. 2003;17(4):617–636. doi: 10.1142/S0218001403002538
  • Kwag HK, Kim SH, Jeong SH, et al. Efficient skew estimation and correction algorithm for document images. Image Vis Comput. 2002;20(1):25–35. doi: 10.1016/S0262-8856(01)00071-3
  • Singh C, Bhatia N, Kaur A. Hough transform based fast skew detection and accurate skew correction methods. Pattern Recognit. 2008;41(12):3528–3546. doi: 10.1016/j.patcog.2008.06.002
  • Postl W. Detection of linear oblique structures and skew scan in digitized documents. Proceedings of 8th International Conference on Pattern Recognition; Washington, DC: IEEE Computer Society Press; 1986. p. 687–689.
  • Bloomberg DS, Kopec GE. U.S. Patent No. 5,187,753. Washington (DC): U.S. Patent and Trademark Office; 1993.
  • Cao Y, Wang S, Li H. Skew detection and correction in document images based on straight-line fitting. Pattern Recognit Lett. 2003;24(12):1871–1879. doi: 10.1016/S0167-8655(03)00010-2
  • Liu H, Wu Q, Zha H, et al. Skew detection for complex document images using robust borderlines in both text and non-text regions. Pattern Recognit Lett. 2008;29(13):1893–1900. doi: 10.1016/j.patrec.2008.06.008
  • Okun O, Pietikäinen M, Sauvola J. Document skew estimation without angle range restriction. Int J Doc Anal Recogn. 1999;2(2-3):132–144. doi: 10.1007/s100320050043
  • Papandreou A, Gatos B. Slant estimation and core-region detection for handwritten Latin words. Pattern Recognit Lett. 2014;35:16–22. doi: 10.1016/j.patrec.2012.08.005
  • Vinciarelli A, Luettin J. A new normalization technique for cursive handwritten words. Pattern Recognit Lett. 2001;22(9):1043–1050. doi: 10.1016/S0167-8655(01)00042-3
  • Pastor M, Toselli A, Vidal E. Projection profile based algorithm for slant removal. International Conference image analysis and Recognition. Berlin: Springer; 2004, September. p. 183–190.
  • Kavallieratou E, Fakotakis N, Kokkinakis G. Slant estimation algorithm for OCR systems. Pattern Recognit. 2001;34(12):2515–2522. doi: 10.1016/S0031-3203(00)00153-9
  • Ding Y, Ohyama W, Kimura F, et al. Local slant estimation for handwritten English words. Ninth International Workshop on Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Kokubunji, Tokyo, Japan: IEEE; 2004. p. 328–333.
  • Bertolami R, Uchida S, Zimmermann M, et al. Non-uniform slant correction for handwritten text line recognition. Ninth International Conference on Document Analysis and Recognition, 2007. ICDAR 2007. Vol. 1. Curitiba, Paraná, Brazil: IEEE; 2007. p. 18–22.
  • Rehman A, Mohammad D, Sulong G, et al. Simple and effective techniques for core-region detection and slant correction in offline script recognition. 2009 IEEE International Conference on Signal and image processing Applications (ICSIPA). Kuala Lumpur, Malaysia: IEEE; 2009. p. 15–20.
  • Eynard L, Emptoz H. Italic or roman: word style recognition without a priori knowledge for old printed documents. 10th International Conference on Document Analysis and Recognition, 2009. ICDAR'09. Barcelona, Spain: IEEE; 2009, 26–29 July. p. 823–827.
  • Taira E, Uchida S, Sakoe H. Nonuniform slant correction for handwritten word recognition. IEICE Trans Inf Syst. 2004;87(5):1247–1253.
  • Uchida S, Taira E, Sakoe H. Nonuniform slant correction using dynamic programming. Proceedings of Sixth International Conference on Document analysis and Recognition, 2001. Seattle, WA: IEEE Computer Society; 2001. p. 434–438.
  • Ziaratban M, Faez K. Non-uniform slant estimation and correction for Farsi/Arabic handwritten words. Int J Document Anal Recognit (IJDAR). 2009;12(4):249. doi: 10.1007/s10032-009-0092-x
  • Jana P, Ghosh S, Bera SK, et al. Handwritten document image binarization: an adaptive K-means based approach. 2017 IEEE Calcutta Conference (CALCON). Kolkata, India: IEEE; 2017, 2–3 December. p. 226–230.
  • Quandt RE. The estimation of the parameters of a linear regression system obeying two separate regimes. J Am Stat Assoc. 1958;53(284):873–880. doi: 10.1080/01621459.1958.10501484
  • Bozinovic RM, Srihari SN. Off-line cursive script word recognition. IEEE Trans Pattern Anal Mach Intell. 1989;11(1):68–83. doi: 10.1109/34.23114

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.