Skip to main content
Log in

GPU parallel implementation of the new hybrid binarization based on Kmeans method (HBK)

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

The Optical Character Recognition (OCR) is a process that converts characters within images into text documents. In paperless applications, OCR systems have to ensure a better accuracy as well as a high speed. One of the most important steps in OCR is binarization. In this context, we proposed recently the hybrid binarization-based Kmeans method (HBK) (Soua et al. in International Symposium on Communications, Control, and Signal Processing, 2014). HBK offers a satisfying recognition rate while scoring 91 % accuracy. In the other hand, running on an Intel Core i3 CPU processor, the HBK requires at least 1.9 s to process one A4 300 dpi document. However, binarization step should not exceed 460 ms in our real-time OCR system. For this, we propose in this paper a parallel implementation of the HBK method on the NVIDIA GTX 660 graphic processing unit (GPU). Our implementation combines fine-grained and coarse-grained parallelism strategies for the best GPU use. In addition, the costly CPU–GPU communication overhead is avoided and an efficient memory management is ensured. The effectiveness of our implementation is validated through extensive experiments, which demonstrate that the proposed HBK parallelization accelerates the studied process. Indeed, we ensure the binarization of one document in just 425 ms. Consequently, the implemented design is able to meet the targeted real-time OCR system in paperless application.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Copyright(c) 2012. EPITA and Development Laboratory (LRDE) with permission from Le Nouvel Observateur. LRDE-DBD is available online on the web site: http://www.lrde.epita.fr/cgi-bin/twiki/view/Olena/DatasetDBD.

  2. Le Nouvel Observateur. Issue 2402, November 18–24, 2010 and available on the website: http://tempsreel.nouvelobs.com.

References

  1. Xiu, P., Baird, H.S.: Whole-book recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2467–2480 (2012)

    Article  Google Scholar 

  2. 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)

  3. Collins-Thompson, K., Schweizer, C., Dumais, S.: Improved string matching under noisy channel conditions. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 357–364 (2011)

  4. Eikvil, L.: OCR-Optical Character Recognition (1993). http://www.nr.no/~eikvil/OCR.pdf

  5. 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 

  6. Ashari, E., Hornsey, R.: FPGA implementation of real-time adaptive Image thresholding. In: Proceedings of the Photonic Applications in Astronomy, Biomedicine, Imaging, Materials Processing, and Education (2004)

  7. Fong, W.: Document imaging: a step toward a paperless office. http://web.simmons.edu/~chen/nit/NIT\%2792/133-fon.htm

  8. Kumar, D., et al.: MAPS: midline analysis and propagation of segmentation. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing. Article No. 15 (2012)

  9. Soua, M., Kachouri, R., Akil, M.: A new hybrid binarization method based on Kmeans. In: IEEE International Symposium on Communications, Control, and Signal Processing, pp. 118–123 (2014)

  10. Lazzara, G., Graud, T.: Efficient multiscale Sauvola’s binarization. Int. J. Doc. Anal. Recognit. 2(14), 105–123 (2014)

  11. Niblack, W.: An Introduction to Digital Image Processing. Strandberg Publishing Company, Denmark (1985)

    Google Scholar 

  12. Srinivasan, S., et al.: Performance characterization and acceleration of optical character recognition on handheld. In: IEEE International Symposium on Workload Characterization (IISWC), pp. 1–10 (2010)

  13. Li, Y., Zhao, K., Chu, X., Liu, J.: Speeding up k-means algorithm by GPUs. In: Proceedings of the IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 115–122 (2010)

  14. Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Skadron, K.: A performance study of general-purpose applications on graphics processors using CUDA. J. Parallel Distrib. Comput. 68(10), 1370–1380 (2008)

    Article  Google Scholar 

  15. Fang, W., et al.: Parallel Data Mining on Graphics Processors. Technical report, Hong Kong University of Science and Technology (2008)

  16. Hong-tao, B., Li-li H., Dan-tong O., Zhan-shan L., He, L.: KMeans on commodity GPUs with CUDA. In: Proceedings of the WRI World Congress

  17. Sirotkovic, J., Dujmic H., Papic, V.: K-means image segmentation on massively parallel GPU architecture. In: Proceedings of the 35th International Convention MIPRO, pp. 489–494 (2012)

  18. Pisharath J., Liu, Y., Liao, W.-K., Choudhary, A., Memik, G., Parhi, J.: ’Nu-MineBench 2.0’. In: CUCIS Technical Report Center for Ultra-Scale Computing and Information Security, Northwestern University (2005)

  19. Wu, R., Zhang, B., Hsu, M.: Clustering billions of data points using GPUs. In: Proceeding of the Combined Workshops on Unconventional High Performance Computing Workshop Plus Memory Access Workshop, Ischia, Italy, pp. 1–6 (2009)

  20. Lloyd, S.P.: Least square quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  21. Smith, R.: An overview of the Tesseract OCR engine. In: Proceedings Ninth International Conference on Document Analysis and Recognition (ICDAR), pp. 629–633 (2007)

  22. EPITA and Development Laboratory (LRDE). http://www.lrde.epita.fr/cgi-bin/twiki/view/Olena/DatasetDBD

  23. Lelore, T., Bouchara, F.: Super-resolved binarization of text based on the FAIR algorithm. In: Proceedings of International Conference on Document Analysis and Recognition, vol. 13, pp. 303–314 (2010)

  24. Chen, T.Y., et al.: On the statistical properties of the F-measure, Quality Software, 2004. In: Proceedings of the Fourth International Conference on QSIC 2004, pp. 146–153 (2004)

  25. Fabrizio, J., Marcotegui, B., Cord, M.: Text segmentation in natural scenes using toggle-mapping. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2373–2376 (2009)

  26. NVIDIA.: CUDA C best practices guide (version 4.0), Santa Clara, California, USA (2011). http://www.khronos.org/opencl/

  27. NVidia: CUDA C Programming Guide. http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

  28. Khronos: OpenCL: the open standard for parallel programming of heterogeneous systems. http://www.khronos.org/opencl/

  29. Parallel programming and computing platform, CUDA, NVIDIA. http://www.nvidia.com/object/cuda_hom_new.html

  30. Tompson, J., Schlachter, K.: An introduction to the programming Model. In: Distributed Computing CSCI-GA.2631-001 (2012)

  31. NVIDIAs Next Generation CUDA TM Compute Architecture: Kepler TM GK110. http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf

  32. Yi, Y., Lai, C., Petrov, S.: Efficient parallel CKY parsing using GPUs. J. Log. Comput. 24(2), 375–393 (2014)

  33. Sauvola, J., Seppanen, T., Haapakoski, S., Pietikainen, M.: Adaptive document binarization. In: 4th International Conference on Document Analysis and Recognition, Ulm, Germany, pp. 147–152 (1997)

  34. Singh, B.M., et al.: Parallel implementation of Niblack’s binarization approach on CUDA. Int. J. Comput. Appl. 32(2), 22–27 (2011)

    Google Scholar 

  35. Khurshid, K., et al. Comparison of Niblack inspired binarization methods for ancient documents. In: Proceedings of the 16th Document Recognition and Retrieval Conference, Part of the IS and T-SPIE Electronic Imaging Symposium, San Jose, CA, USA (2009)

  36. Singh, B.M., et al.: Parallel implementation of Souvola’s binarization approach on GPU. Int. J. Comput. Appl. 32(2), 28–33 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahmoud Soua.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soua, M., Kachouri, R. & Akil, M. GPU parallel implementation of the new hybrid binarization based on Kmeans method (HBK). J Real-Time Image Proc 14, 363–377 (2018). https://doi.org/10.1007/s11554-014-0458-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-014-0458-2

Keywords

Navigation