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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 3–13Cite as

Text Extraction Using Sparse Representation over Learning Dictionaries

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

Abstract

This paper presents a new approach for text detection using sparse representation over learned dictionaries. More specifically, the K-SVD algorithm is used for constructing two dictionaries, one for the background and one for the text. Then, text detection is done by comparing the error constructions of each patch of image over two dictionaries. Results on ICDAR dataset present that proposed method is competitive related to state-of-the-art methods.

This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.18.04.

K. C. Santosh—IEEE senior member

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Notes

  1. 1.

    http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Co-mpetitions.

References

  1. Aerschot, W., Jansen, M., Bultheel, A.: Normal mesh based geometrical image compression. Image Vis. Comput. 27(4), 459–468 (2009)

    Article  Google Scholar 

  2. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. Sig. Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  3. Angadi, S., Kodabagi, M.: A texture based methodology for text region extraction from low resolution natural scene images. In: Advance Computing Conference, pp. 121–128 (2010)

    Google Scholar 

  4. Belaid, A., Santosh, K., D’Andecy, V.P.: Handwritten and printed text separation in real document. In: The Thirteenth International Conference on Machine Vision Applications (2013)

    Google Scholar 

  5. Bui, T., Pan, W., Suen, C.: Text detection from natural scene images using topographic maps and sparse representations. In: The IEEE International Conference on Image Processing (2009)

    Google Scholar 

  6. Chen, D., Jean-Marc, O., Herve, B.: Text detection and recognition in images and video frames. Pattern Recogn. 37(3), 595–608 (2004)

    Article  Google Scholar 

  7. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1), 33–61 (1998)

    Article  MathSciNet  Google Scholar 

  8. Chen, X., Yuille, A.: Detecting and reading text in natural scenes. In: Proceeding of CVPR (2004)

    Google Scholar 

  9. Daubechies, I., Devore, R., Fornasier, M., Gunturk, C.: Iteratively reweighted least squares minimization for sparse recovery. Commun. Pure Appl. Math. 63(1), 1–38 (2009)

    Article  MathSciNet  Google Scholar 

  10. Do, T.H., Tabbone, S., Terrades, O.R.: Text/graphic separation using a sparse representation with multi-learned dictionaries. In: The International Conference on Pattern Recognition, pp. 689–692 (2012)

    Google Scholar 

  11. Do, T.H., Tabbone, S., Terrades, O.R.: Document noise removal using sparse representations over learned dictionary. In: ACM Symposium on Document Engineering, pp. 161–168 (2013)

    Google Scholar 

  12. Donoho, D., Elad, M.: Optimally sparse representation in general (nonorthogonal) dictionaries via ell1 minimization. PNAS 100(5), 2197–2202 (2003)

    Article  Google Scholar 

  13. Elad, M.: Sparse and Redundant Representation: From Theory to Applications in Signal and Images Processing. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7011-4

    Book  MATH  Google Scholar 

  14. Engan, K., Skretting, K., Husoy, J.H.: Family of iterative LS-based dictionary learning algorithm, ILS-DLA, for sparse signal representation. Digit. Signal Process. 17(1), 32–49 (2007)

    Article  Google Scholar 

  15. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proceedings of the CVPR (2010)

    Google Scholar 

  16. Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: towards a system for visually impaired persons. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 683–686 (2004)

    Google Scholar 

  17. Jain, A., Yu, B.: Automatic text location in images and video frames. Pattern Recogn. 31(12), 2055–2076 (1998)

    Article  Google Scholar 

  18. Jiang, R., Qi, F., Xu, L., Wu, G.: Using connected components’ features to detect and segment text. J. Image Graph. 11, 1653–1656 (2006)

    Google Scholar 

  19. Kim, K., Jung, K., Kim, J.: Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1631–1639 (2003)

    Article  Google Scholar 

  20. Kumar, S., Gupta, R., Khanna, N., Chaudhury, S., Joshi, S.: Text extraction and document image segmentation using matched wavelets and MFR model. IEEE Trans. Image Process. 16(8), 2117–2128 (2007)

    Article  MathSciNet  Google Scholar 

  21. Lee, T.W., Lewicki, M.: Unsupervised image classification, segmentation and enhancement using ICA mixture models. IEEE Trans. Image Process. 11(3), 270–279 (2002)

    Article  Google Scholar 

  22. Lienhart, R., Wernicke, A.: Localizing and segmenting text in images and videos. IEEE Trans. Circuits Syst. Video Technol. 12, 256–268 (2002)

    Article  Google Scholar 

  23. Lim, J., Park, J., Medioni, G.: Text segmentation in color images using tensor voting. Image Vis. Comput. 25(5), 671–685 (2007)

    Article  Google Scholar 

  24. Liu, Z., Sarkar, S.: Robust outdoor text detection using text intensity and shape features. In: The 19th International Conference on Pattern Recognition, pp. 1–4 (2008)

    Google Scholar 

  25. Lucas, S.M.: ICDAR 2005 text locating competition results. In: Proceedings of the ICDAR (2005)

    Google Scholar 

  26. Mallat, S.: Geometrical grouplets. Appl. Comput. Harmonic Anal. 26(2), 161–180 (2009)

    Article  MathSciNet  Google Scholar 

  27. Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. Sig. Process. 41(12), 3397–3415 (1993)

    Article  Google Scholar 

  28. Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 710–732 (1992)

    Article  Google Scholar 

  29. Marial, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696 (2009)

    Google Scholar 

  30. Pan, W., Bui, T., Suen, C.: Text detection from scene images using sparse representation. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–5 (2008)

    Google Scholar 

  31. Pan, Y., Liu, C., Hou, X.: Fast scene text localization by learning-based filtering and verification. In: The 17th IEEE International Conference on Image Processing, pp. 2269–2272 (2010)

    Google Scholar 

  32. Park, J., Chung, H., Seong, Y.: Scene text detection suitable for parallelizing on multi-core. In: IEEE International Conference on Image Processing, pp. 2425–2428 (2009)

    Google Scholar 

  33. Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 40–44 (1993)

    Google Scholar 

  34. Santosh, K.C.: g-DICE: graph mining-based document information content exploitation. IJDAR 18(4), 337–355 (2015)

    Article  Google Scholar 

  35. Santosh, K.C.: Document Image Analysis. Current Trends and Challenges in Graphics Recognition. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2339-3

    Book  Google Scholar 

  36. Skretting, K., Engan, K.: Recursive least squares dictionary learning algorithm. Sig. Process. 58(4), 2121–2130 (2010)

    Article  MathSciNet  Google Scholar 

  37. Temlyakov, V.N.: Weak greedy algorithms. Adv. Comput. Math. 12(2–3), 213–227 (2000)

    Article  MathSciNet  Google Scholar 

  38. Hoang, T.V., Tabbone, S.: Text extraction from graphical document images using sparse representation. In: Proceedings of the 9th International Workshop on Document Analysis Systems (2010)

    Google Scholar 

  39. Wright, J., Ganesh, A., Yang, A., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  40. Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting text of arbitrary orientations in natural images. In: Proceedings of CVPR (2012)

    Google Scholar 

  41. Ye, Q., Jiao, J., Huang, J., Yu, H.: Text detection and restoration in natural scene images. J. Vis. Commun. Image Represent. 18(6), 504–513 (2007)

    Article  Google Scholar 

  42. Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. In: Image Processing (2011)

    Google Scholar 

  43. Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Trans. Image Process. 20(9), 2594–2605 (2011)

    Article  MathSciNet  Google Scholar 

  44. Zhao, M., Li, S., Kwok, J.: Text dectection in images using sparse representation with discriminative dictioanries. Image Vis. Comput. 28, 1590–1599 (2010)

    Article  Google Scholar 

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Acknowledgements

This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.18.04.

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Authors and Affiliations

  1. Department of Informatics, VNU University of Science, Hanoi, Vietnam

    Thanh-Ha Do & Thi Minh Huyen Nguyen

  2. Department of Computer Science, University of South Dakota, Vermillion, USA

    K. C. Santosh

Authors
  1. Thanh-Ha Do
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  2. Thi Minh Huyen Nguyen
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  3. K. C. Santosh
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Corresponding author

Correspondence to Thanh-Ha Do .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Do, TH., Nguyen, T.M.H., Santosh, K.C. (2019). Text Extraction Using Sparse Representation over Learning Dictionaries. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_1

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_1

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