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Artistic Multi-character Script Identification Using Iterative Isotropic Dilation Algorithm

  • Mridul GhoshEmail author
  • Sk Md Obaidullah
  • K. C. Santosh
  • Nibaran Das
  • Kaushik Roy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

In this work, a new problem of script identification named artistic multi-character script identification has been addressed. Two types of datasets of artistic documents/images prepared with Bangla, Devanagari and Roman script have been used: one is real life artistic multi-character script image and another is synthetic artistic multi-character script image. After binarization using Otsu’s algorithm, some character images found to be broken into components. To overcome this, a novel iterative isotropic dilation algorithm is proposed here to convert the components into a single component object. Then two types of features, namely shape based and texture based features have been considered. Discrete Gabor wavelet has been exploited with 2 scales and 4 orientations for texture feature extraction and PCA is used to reduce the dimensionality of the texture feature space. The performance of the proposed algorithm has been tested with different machine learning classifiers and promising accuracy has been observed.

Keywords

Script identification Multi-character script Otsu’s binarization method Random forest Multilayer perceptron 

References

  1. 1.
    Ghosh, D., Dube, T., Shivaprasad, A.: Script recognition-a review. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2142–2161 (2010)CrossRefGoogle Scholar
  2. 2.
    Obaidullah, S.M., Santosh, K.C., Das, N., Halder, C., Roy, K.: Handwritten Indic script identification in multi-script document images: a survey. Int. J. Pattern Recognit. Artif. Intell. 32, 1856012 (2018)CrossRefGoogle Scholar
  3. 3.
    Obaidullah, S.M., Bose, A., Mukherjee, H., Santosh, K.C., Das, N., Roy, K.: Extreme learning machine for handwritten Indic script identification in multi-script documents. J. Electron. Imaging 27(5), 051214 (2018)CrossRefGoogle Scholar
  4. 4.
    Obaidullah, S.M., Halder, C., Santosh, K.C., Das, N., Roy, K.: Automatic line-level script identification from handwritten document images-a region-wise classification framework for Indian subcontinent. Malaysian J. Comput. Sci. 31(1), 63–84 (2018)CrossRefGoogle Scholar
  5. 5.
    Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. Int. J. Mach. Learn. Cybern. 10, 1–20 (2017)Google Scholar
  6. 6.
    Obaidullah, S.K., Santosh, K.C., Halder, C., Das, N., Roy, K.: Word-level multi-script Indic document image dataset and baseline results on script identification. Int. J. Comput. Vis. Image Process. (IJCVIP) 7(2), 81–94 (2017)CrossRefGoogle Scholar
  7. 7.
    Obaidullah, S.M., Halder, C., Santosh, K.C., Das, N., Roy, K.: PHDIndic\(\_\)11: pagelevel handwritten document image dataset of 11 official Indic scripts for script identification. Multimedia Tools Appl. 77(2), 1643–1678 (2018)CrossRefGoogle Scholar
  8. 8.
    Obaidullah, S.M., Halder, C., Das, N., Roy, K.: Bangla and Oriya script lines identification from handwritten document images in tri-script scenario. Int. J. Serv. Sci. Manag. Eng. Technol. (IJSSMET) 7(1), 43–60 (2016)Google Scholar
  9. 9.
    Mori, S., Suen, C.Y., Yamamoto, K.: Historical review of OCR research and development. Proc. IEEE 80(7), 1029–1058 (1992)CrossRefGoogle Scholar
  10. 10.
    Rajput, G.G., Anita, H.B.: Handwritten script identification from a bi-script document at line level using Gabor filters. In: Proceedings of SCAKD, pp. 94–101 (2011)Google Scholar
  11. 11.
    Aithal, P.K., Rajesh, G., Acharya, D.U., Krishnamoorthi, M., Subbareddy, N.V.: Script identification for a tri-lingual document. In: Das, V.V., Stephen, J., Chaba, Y. (eds.) CNC 2011. CCIS, vol. 142, pp. 434–439. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19542-6_82CrossRefGoogle Scholar
  12. 12.
    Pal, U., Sinha, S., Chaudhuri, B.B.: Multi-script line identification from Indian documents. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings, pp. 880. IEEE (2003)Google Scholar
  13. 13.
    Pati, P.B., Ramakrishnan, A.G.: Word level multi-script identification. Pattern Recogn. Lett. 29(9), 1218–1229 (2008)CrossRefGoogle Scholar
  14. 14.
    Dhanya, D., Ramakrishnan, A.G.: Script identification in printed bilingual documents. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 13–24. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45869-7_2CrossRefGoogle Scholar
  15. 15.
    Dhanya, D., Ramakrishnan, A.G.: Optimal feature extraction for bilingual OCR. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 25–36. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45869-7_3CrossRefGoogle Scholar
  16. 16.
    Pati, P.B., Raju, S.S., Pati, N., Ramakrishnan, A.G.: Gabor filters for document analysis in Indian bilingual documents. In: Proceedings of International Conference on Intelligent Sensing and Information Processing, 2004, pp. 123–126. IEEE (2004)Google Scholar
  17. 17.
    Mohanty, S., Dasbebartta, H.N., Behera, T.K.: An efficient bi-lingual optical character recognition (English-Oriya) system for printed documents. In: Seventh International Conference on Advances in Pattern Recognition, 2009. ICAPR 2009, pp. 398–401. IEEE (2009)Google Scholar
  18. 18.
    Pal, U., Roy, P.P., Tripathy, N., Lladós, J.: Multi-oriented Bangla and Devanagari text recognition. Pattern Recogn. 43(12), 4124–4136 (2010)CrossRefGoogle Scholar
  19. 19.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  20. 20.
    Chang, F., Chen, C.J., Lu, C.J.: A linear-time component-labeling algorithm using contour tracing technique. Computer Vis. Image Underst. 93(2), 206–220 (2004)CrossRefGoogle Scholar
  21. 21.
    Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans. Pattern Anal. Mach. Intell. 10(4), 579 (1988)CrossRefGoogle Scholar
  22. 22.
    Silvela, J., Portillo, J.: Breadth-first search and its application to image processing problems. IEEE Trans. Image Process. 10(8), 1194–1199 (2001)CrossRefGoogle Scholar
  23. 23.
    Breu, H., Gil, J., Kirkpatrick, D., Werman, M.: Linear time Euclidean distance transform algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 529–533 (1995)CrossRefGoogle Scholar
  24. 24.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)CrossRefGoogle Scholar
  25. 25.
    Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. In: Yin, P.-Y. (ed.) Pattern Recognition, pp. 43–90. In-Tech (2008)Google Scholar
  26. 26.
    Harmsen, J.J., Pearlman, W.A.: Steganalysis of additive-noise modelable information hiding. In: Security and Watermarking of Multimedia Contents V, vol. 5020, pp. 131–143. International Society for Optics and Photonics (2003)Google Scholar
  27. 27.
    Ma, W.Y., Manjunath, B.S.: Texture features and learning similarity. In: 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996. Proceedings CVPR 1996, pp. 425–430. IEEE (1996)Google Scholar
  28. 28.
    Zhang, D., Wong, A., Indrawan, M., Lu, G.: Content-based image retrieval using Gabor texture features. IEEE Trans. PAMI 13–15 (2000)Google Scholar
  29. 29.
    Jolliffe, I.T.: Principal Component Analysis. Springer Series in Statistics, 2nd edn, p. XXIX, 487. Springer, New York (2002).  https://doi.org/10.1007/b98835CrossRefzbMATHGoogle Scholar
  30. 30.
    Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)CrossRefGoogle Scholar
  31. 31.
    Martinez, A.M., Kak, A.C.: PCA versus LDA (PDF). IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)CrossRefGoogle Scholar
  32. 32.
    Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12(10), 2385–2404 (2000)CrossRefGoogle Scholar
  33. 33.
    Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P.: An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogrammetry Remote Sens. 67, 93–104 (2012)CrossRefGoogle Scholar
  34. 34.
    Franke, J., Mandler, E.: A comparison of two approaches for combining the votes of cooperating classifiers. In: 11th IAPR International Conference on Pattern Recognition. Vol. II. Conference B: Pattern Recognition Methodology and Systems, pp. 611–614. IEEE (1992)Google Scholar
  35. 35.
    Gardezi, S.J.S., Faye, I., Eltoukhy, M.M.: Analysis of mammogram images based on texture features of curvelet sub-bands. In: Fifth International Conference on Graphic and Image Processing (ICGIP 2013), vol. 9069, p. 906924. International Society for Optics and Photonics (2014)Google Scholar
  36. 36.
    Alkan, A., Koklukaya, E., Subasi, A.: Automatic seizure detection in EEG using logistic regression and artificial neural network. J. Neurosci. Methods 148(2), 167–176 (2005)CrossRefGoogle Scholar
  37. 37.
    Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011)CrossRefGoogle Scholar
  38. 38.
    Santosh, K.C., Wendling, L.: character recognition based on non-linear multi-projection profiles measure. Frontiers Comput. Sci. 9(5), 678–690 (2015)CrossRefGoogle Scholar
  39. 39.
    Santosh, K.C.: Character recognition based on DTW-radon. In: ICDAR, pp. 264–268 (2011)Google Scholar
  40. 40.
    K.C., S., Lamiroy, B., Wendling, L.: DTW for matching radon features: a pattern recognition and retrieval method. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 249–260. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23687-7_23CrossRefGoogle Scholar
  41. 41.
    Santosh, K.C., Lamiroy, B., Wendling, L.: DTW-radon-based shape descriptor for pattern recognition. IJPRAI 27(3) (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mridul Ghosh
    • 1
    Email author
  • Sk Md Obaidullah
    • 2
  • K. C. Santosh
    • 3
  • Nibaran Das
    • 4
  • Kaushik Roy
    • 5
  1. 1.Department of Computer ScienceShyampur Siddheswari MahavidyalayaHowrahIndia
  2. 2.Department of Computer Science and EngineeringAliah UniversityKolkataIndia
  3. 3.Department of Computer ScienceUniversity of South DakotaVermillionUSA
  4. 4.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  5. 5.Department of Computer ScienceWest Bengal State UniversityBarasatIndia

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