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GPU Approach for Handwritten Devanagari Document Binarization

  • Sandhya AroraEmail author
  • Sunita Jahirabadkar
  • Anagha Kulkarni
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)

Abstract

The optical character recognition (OCR) is the process of converting scanned images of machine printed or handwritten text, numerals, letters, and symbols into a computer processable format such as ASCII. For creating OCR’s paperless application, a system of high speed and of better accuracy is required. Parallelization of algorithm using graphics processing unit (GPU) along with CPU can be used to speed up the processing. In GPU computing, the compute-intensive operations are performed on GPU while serial code still runs on CPU. Binarization is one of the most fundamental preprocessing techniques in the area of image processing and pattern recognition. This paper proposes an adaptive threshold binarization algorithm for GPU. The aim of this research work is to speed up binarization process that eventually will help to accelerate the processing of document recognition. The algorithm implementation is done using Compute Unified Device Architecture (CUDA) software interface by NVIDIA. An average speedup of 2× is achieved on GPU GeForce 210 having 16 CUDA cores and 1.2 compute level, over the serial implementation.

Keywords

CUDA GPU OCR Binarization Parallelization Pattern recognition 
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References

  1. 1.
    I.K. Sethi and B. Chatterjee, “Machine Recognition of constrained Handprinted Devnagari”, Pattern Recognition, Vol. 9, pp. 69–75, 1977.Google Scholar
  2. 2.
    Pal, U. and Chaudhuri, B.B., “Indian script character recognition: a survey”. Pattern Recognition 37, 1887–1899, 2004.Google Scholar
  3. 3.
    S. Arora, “Studies on some Soft Computing Techniques: A Case Study for Constrained Handwritten Devnagari Characters and Numerals”, International Journal of Scientific & Engineering Research, Volume 4, Issue 7, 2013.Google Scholar
  4. 4.
    Xiu, P., Baird, H.S.: Whole-book recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2467–2480, 2012.Google Scholar
  5. 5.
    Kae, A., Huang, G.B., Doersch, C., Learned-Miller, E.G., “Improving state-of-the-art OCR through high-precision document-specific modeling” In: CVPR, pp. 1935–1942,2010.Google Scholar
  6. 6.
    Eikvil, L. “OCR-Optical Character Recognition”, 1993, http://www.nr.no/*eikvil/OCR.pdf.
  7. 7.
    Gaceb, D., Eglin, V., Lebourgeois, F. “A new mixed binarization method used in real time application of automatic business document and postal mail sorting” Int. Arab J. Inf. Technol. 10(2), 179–188, 2013.Google Scholar
  8. 8.
    M. Hanmandlu and O.V. Ramana Murthy, “Fuzzy Model Based Recognition of Handwritten Hindi Numerals”, In Proc. Intn Conference on Cognition and Recognition, 490–496, 05.Google Scholar
  9. 9.
    M. Hanmandlu, O.V. Ramana Murthy, Vamsi Krishna Madasu, “Fuzzy Model based recognition of Handwritten Hindi characters”, IEEE Computer society, Digital Image Computing Techniques and Applications, 2007.Google Scholar
  10. 10.
    Reena Bajaj, Lipika Dey, and S. Chaudhury, “Devnagari numeral recognition by combining decision of multiple connectionist classifiers”, Sadhana, Vol. 27, part. 1, pp.-59–72, 02.Google Scholar
  11. 11.
    S. Kumar, C. Singh, “A Study of Zernike Moments and its use in Devnagari Handwritten Character Recognition”, Intl. Conf. On Cognition and Recognition, 514–520, 05.Google Scholar
  12. 12.
    U. Bhattacharya, B. B. Chaudhuri, R. Ghosh and M. Ghosh, “On Recognition of Handwritten Devnagari Numerals”, In Proc. of the Workshop on Learning Algorithms for Pattern Recognition, Sydney, pp. 1–7, 2005.Google Scholar
  13. 13.
    N. Sharma, U. Pal, F. Kimura, S. pal, “Recognition of Off Line Handwritten Devnagari Characters using Quadratic Classifier”, ICCGIP 2006, LNCS 4338, pp 805–816, 2006.Google Scholar
  14. 14.
    S. Arora, D. Bhattacharya, M. Nasipuri, “ Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance”, International Journal of Computer Science and Security (IJCSS), Volume (4): Issue (1), 2010.Google Scholar
  15. 15.
    Brij Mohan Singh, Rahul Sharma, Ankush Mittal, Debashish Ghosh, “Parallel Implementation of Otsu’s Binarization Approach on GPU”, International Journal of Computer Applications (0975 – 8887) Volume 32–No. 2, October 2011.Google Scholar
  16. 16.
    Sauvola, J. and Pietikainen, M., “Adaptive document image binarization” Pattern Recognition, Vol. 33, 225–236, 2000.Google Scholar
  17. 17.
    Kim, I.K., Jung, D.W. and Park, R.H., “Document image binarization based on topographic analysis using a water flow model”, Pattern Recognition, Vol. 35, 265–277, 2002.Google Scholar
  18. 18.
    Gatos, B., Pratikakis, I. and Perantonis, S. J. “Adaptive degraded document image binarization”, Pattern Recognition, Vol. 39, 317–327, 2006.Google Scholar
  19. 19.
    Chang, Y.F., Pai, Y.T. and Ruan, S.J. “An efficient thresholding algorithm for degraded document images based on intelligent block detection”, IEEE Intern Conf on Systems, Man, and Cybernetics, 667–672, 08.Google Scholar
  20. 20.
    Valizadeh, M., Komeili, M., Armanfard, N. and Kabir, E., “Degraded document image binarization based on combination of two complementary algorithms”, International Conf. of Advances in Computational Tools for Engineering Applications, IEEE, 595–599, 2009.Google Scholar
  21. 21.
    Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A. E. and Purcell, T. J., “A survey of general-purpose computation on graphics hardware”, In proceeding of Eurographics, State of the Art Reports, 21–51, 2005.Google Scholar
  22. 22.
    M. Soua, R. Kachouri, M. Akil, “ GPU parallel implementation of the new hybrid binarization based on Kmeans method HBK”, Journal of Real-Time Image Processing, 14.Google Scholar
  23. 23.
    Yi, Y., Lai, C., Petrov, S.: Efficient parallel CKY parsing using GPUs. J. Log. Comput. 24(2), 375–393, 2014.Google Scholar
  24. 24.
    George Cherian Panappally, M.S. Dhanesh, “Design of graphics processing unit for image processing”, 1st Intern Conf on Computational Systems & Communications, 2014.Google Scholar
  25. 25.
    Larsen, E. S., McAllister, D.,” Fast Matrix Multiplies using Graphics Hardware”, International Conference for High Performance Computing and Communications, 159–168, 2001.Google Scholar
  26. 26.
    Trendall C. and Stewart, A. J., “General calculations using graphics hardware with applications to interactive caustics Rendering Techniques”, 11th Eurographics Workshop on Rendering, 287–298, 2000.Google Scholar
  27. 27.
    Li, Wei, Xiaoming, A. & Kaufman, “Implementing lattice boltzmann computation on graphics h/w,” Intern. Conf. for High Performance Computing and Communins, 2001.Google Scholar
  28. 28.
    Kruger, J. and Westermann, R., “Linear operators for GPU implementation of numerical algorithms”, In Proceedings of SIGGRAPH, San Diego, 908–916, 2003.Google Scholar
  29. 29.
    Mizukami, Y., Koga, K. and Torioka, T., “A handwritten character recognition system using hierarchical extraction of displacement”, IEICE, J77-D-II(12): 2390–2393, 1994.Google Scholar
  30. 30.
    Steinkraus, D., Buck, I., and Simard, P. Y., “GPUs for machine learning algorithms”, International Conference of Document Analysis and Recognition, 1115–1120, 2005.Google Scholar
  31. 31.
    Ilie, “A. Optical character recognition on graphics hardware”, www.cs.unc.edu/~adyilie/IP/Final.pdf.
  32. 32.
    Lazzara, G., Graud, T., “Efficient multiscale Sauvola’s binarization”, Int. Journal of Document Analysis and Recognition, 2(14), 105–123,2014.Google Scholar
  33. 33.
    Oh, K.S. and Jung, K., “GPU implementation of neural networks”, Pattern Recognition, Elsevier, 1311–1314, 2004.Google Scholar
  34. 34.
    Jung, K. “Neural Network-based text localization in color image”,. Pattern Recognition Letters, Vol. 22, (4), 1503–1515, 2001.Google Scholar
  35. 35.
    Singh, B.M., Mittal A. and Ghosh, D., “Parallel implementation of Devanagari text line and word segmentation approach on GPU”, IJCA 24(9): 7–14, 2011.Google Scholar
  36. 36.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sandhya Arora
    • 1
    Email author
  • Sunita Jahirabadkar
    • 1
  • Anagha Kulkarni
    • 2
  1. 1.Department of Computer EngineeringCummins College of Engineering for WomenPuneIndia
  2. 2.IT DepartmentCummins College of Engineering for WomenPuneIndia

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