[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Character recognition based on non-linear multi-projection profiles measure

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi-projection profiles that are produced from the Radon transform. The idea is to use dynamic time warping (DTW) algorithm to match corresponding pairs of the Radon features for all possible projections. By using DTW, we can avoid compressing feature matrix into a single vector which may miss information. It can handle character images in different shapes and sizes that are usually happened in natural handwriting in addition to difficulties such as multi-class similarities, deformations and possible defects. Besides, a comprehensive study is made by taking a major set of state-of-the-art shape descriptors over several character and numeral datasets from different scripts such as Roman, Devanagari, Oriya, Bangla and Japanese-Katakana including symbol. For all scripts, the method shows a generic behaviour by providing optimal recognition rates but, with high computational cost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Plamondon R, Srihari S N. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22: 63–84

    Article  Google Scholar 

  2. Arica N, Yarman-Vural F. An overview of character recognition focused on off-line handwriting. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2001, 31(2): 216–233

    Article  Google Scholar 

  3. Suen C Y, Berthod M, Mori S. Automatic recognition of handprinted characters —the state of the art. Proceedings of the IEEE, 1980, 68(4): 469–487

    Article  Google Scholar 

  4. Schantz H F. The History of OCR. Manchester Center, VT: Recognition Technologies Users Association, 1982

    Google Scholar 

  5. Davis R H, Lyall J. Recognition of handwritten characters — a review. Image and Vision Computing, 1986, 4: 208–218

    Article  Google Scholar 

  6. Govindan V, Shivaprasad A. Character recognition —a review. Pattern Recognition, 1990, 23(7): 671–683

    Article  Google Scholar 

  7. Dai R, Liu C, Xiao B. Chinese character recognition: history, status and prospects. Frontiers of Computer Science, 2007, 1(2): 126–136

    Article  Google Scholar 

  8. Trier D, Jain A K, Taxt T. Feature extraction methods for character recognition — a survey. Pattern Recognition, 1996, 29(4): 641–662

    Article  Google Scholar 

  9. Heutte L, Paquet T, Moreau J V, Lecourtier Y, Olivier C. A structural/statistical feature based vector for handwritten character recognition. Pattern Recognition Letters, 1998, 19(7): 629–641

    Article  Google Scholar 

  10. Foggia P, Sansone C, Tortorella F, Vento M. Combining statistical and structural approaches for handwritten character description. Image and Vision Computing, 1999, 17(9): 701–711

    Article  Google Scholar 

  11. Jain A K, Duin R P W, Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22(1): 4–37

    Article  Google Scholar 

  12. Shinghal R, Suen C. A method for selecting constrained hand-printed character shapes for machine recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, 4(1): 74–78

    Article  Google Scholar 

  13. Mantas J, Heaton A. Handwritten character recognition by parallel labelling and shape analysis. Pattern Recognition Letters, 1983, 1: 465–468

    Article  Google Scholar 

  14. Campos T E, Babu B R, Varma M. Character recognition in natural images. In: Proceedings of the International Conference on Computer Vision Theory and Applications. 2009, 273–280

    Google Scholar 

  15. Mahmoud S A. Arabic character recognition using fourier descriptors and character contour encoding. Pattern Recognition, 1994, 27(6): 815–824

    Article  Google Scholar 

  16. Granlund G H. Fourier preprocessing for hand print character recognition. IEEE Transactions on Computers, 1972, C-21(2): 195–201

    Article  MathSciNet  Google Scholar 

  17. Lai M T, Suen C Y. Automatic recognition of characters by fourier descriptors and boundary line encodings. Pattern Recognition, 1981, 14(1–6): 383–393

    Article  Google Scholar 

  18. Rauber T, Steiger Garcao A. Shape description by unl fourier featuresan application to handwritten character recognition. In: Proceedings of the 11th IAPR International Conference on Pattern Recognition Methodology and Systems. 1992, II:466–469

    Google Scholar 

  19. Hopkins J, Andersen T L. A fourier-descriptor-based character recognition engine implemented under the gamera open-source document-processing framework. In: Proceedings of the International Conference on Document Recognition and Retrieval Conference. 2005, 111–118

    Chapter  Google Scholar 

  20. Bernier T, Landry J A. A new method for representing and matching shapes of natural objects. Pattern Recognition, 2003, 36(8): 1711–1723

    Article  Google Scholar 

  21. Kopf S, Haenselmann T, Effelsberg W. Enhancing curvature scale space features for robust shape classification. In: Proceedings of the IEEE International Conference on Multimedia and Expo. 2005, 478–481

    Google Scholar 

  22. Khoddami M, Behrad A. Farsi and latin script identification using curvature scale space features. In: Proceedings of the Symposium on Neural Network Applications in Electrical Engineering. 2010, 213–217

    Chapter  Google Scholar 

  23. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509–522

    Article  Google Scholar 

  24. Tepper M, Acevedo D, Goussies N A, Jacobo J C, Mejail M. A decision step for shape context matching. In: Proceedings of the IEEE International Conference on Image Processing. 2009, 409–412

    Google Scholar 

  25. Flusser J. On the independence of rotation moment invariants. Pattern Recognition, 2000, 33(9): 1405–1410

    Article  Google Scholar 

  26. Kim W Y, Kim Y S. A region-based shape descriptor using zernike moments. Signal Processing: Image Communication, 2000, 16(1–2): 95–102

    Google Scholar 

  27. Zhang D, Lu G. Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication, 2002, 17: 825–848

    Google Scholar 

  28. Frejlichowski D. Analysis of four polar shape descriptors properties in an exemplary application. In: Proceedings of the International conference on Computer Vision and graphics. 2010, 376–383

    Google Scholar 

  29. Zhang D, Lu G. Review of shape representation and description techniques. Pattern Recognition, 2004, 37(1): 1–19

    Article  MATH  Google Scholar 

  30. Tabbone S, Wendling L, Salmon J P. A new shape descriptor defined on the radon transform. Computer Vision and Image Understanding, 2006, 102(1): 42–51

    Article  Google Scholar 

  31. Deans S R. Applications of the Radon Transform. New York: Wiley Interscience Publications, 1983

    MATH  Google Scholar 

  32. Kruskall J B, Liberman M. The symmetric time warping algorithm: From continuous to discrete. In: Proceedings of Time Warps, String Edits and Macromolecules: the Theory and Practice of String Comparison. 1983, 125–161

    Google Scholar 

  33. Keogh E J, Pazzani M J. Scaling up dynamic time warping to massive dataset. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases. 1999, 1–11

    Google Scholar 

  34. Coetzer J. Off-line Signature Verification. Dissertation for PhD Degree, University of Stellenbosch, 2005

    Google Scholar 

  35. Jayadevan R, Kolhe S R, Patil P M. Dynamic time warping based static hand printed signature verification. Pattern Recognition Research, 2009, 4(1): 52–65

    Article  Google Scholar 

  36. Santosh K C, Lamiroy B, Wendling L. DTW for matching radon features: a pattern recognition and retrieval method. In: International Conference on Advances Concepts for Intelligent Vision Systems. 2011, 249–260

    Google Scholar 

  37. Santosh K C. Character recognition based on DTW-radon. In: Proceedings of International Conference on Document Analysis and Recognition. 2011, 264–268

    Google Scholar 

  38. Santosh K C, Lamiroy B, Wendling L. DTW-radon-based shape descriptor for pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 2013, 27(03): 1350008

    Article  MathSciNet  Google Scholar 

  39. Alginahi Y. Preprocessing techniques in character recognition. Character Recognition, 2010, 1–20

    Google Scholar 

  40. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66

    Article  MathSciNet  Google Scholar 

  41. Keogh E J. Exact indexing of dynamic time warping. In: Proceedings of the 28th International Conference on Very Large Data Bases. 2002, 406–417

    Chapter  Google Scholar 

  42. Bhowmik T K, Parui S K, Bhattacharya U, Shaw B. An hmm based recognition scheme for handwritten oriya numerals. In: Proceedings of the International Conference in Information Technology. 2006, 105–110

    Google Scholar 

  43. Bhattacharya U, Chaudhuri B B. Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(3): 444–457

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. US National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, 20894, USA

    K. C. Santosh

  2. SIP-LIPADE, Université Paris Descartes (Paris V), Paris Cedex 06, 75270, France

    Laurent Wendling

Authors
  1. K. C. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurent Wendling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. C. Santosh.

Additional information

K C Santosh is currently a research fellow at the US National Library of Medicine (NLM), National Institutes of Health (NIH), USA. Before this, K C worked as a postdoctoral research scientist at the Université de Lorraine — LORIA (UMR-7503) Campus Scientifique and ITESOFT, France. He earned his PhD in Computer Science from INRIA Nancy Grand Est, Université de Lorraine, France in 2011, MS in information technology by research and thesis from the school of ICT, SIIT, Thammasat University, Thailand in 2007, and BS in electronics and communication from PU, Nepal, in 2003. His research interests include document image analysis, document information content exploitation, biometrics (such as face) and (bio)medical image analysis.

Laurent Wendling received the PhD in computer science from the University of Paul Sabatier, Toulouse, France in 1997. He received the HDR degree in 2006. From 1993 to 1999, he was with the IRIT in the field of pattern recognition. From 1999 to 2009, he was an assistant professor at the ESIAL Nancy, and a member of LORIA in the field of symbol recognition. His current research topics are spatial relation, feature selection, and image segmentation. He is currently a full professor at the Paris Descartes University, in the field of computer science. He is also the group leader of the SIP team, LIPADE.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santosh, K.C., Wendling, L. Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9, 678–690 (2015). https://doi.org/10.1007/s11704-015-3400-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-015-3400-2

Keywords

Search

Navigation