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Script Identification of Movie Titles from Posters

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2020)

Abstract

In this work, a new problem of script identification in movie posters has been addressed. Movie posters contain an amalgamation of different types of objects like images of actors, sceneries, different graphic symbols, several texts having disparate fonts, colors, textures, etc. Such a complex set of components makes it a challenging task in the automatic identification of the script of the movie titles for further processing. Before identifying the script of the titles, localization of the texts is very much necessary. Using transfer learning and non-maximum suppression the text localization has been performed followed textural feature-based script identification among Bangla, Devanagari, and Roman. We experimented with these poster images from Tollywood, Bollywood, and Hollywood and obtained the highest accuracy of 90.65%.

K. C. Santosh—Senior Member in IEEE.

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References

  1. Peng, X., Cao, H., Setlur, S., Govindaraju, V., Natarajan, P.: Multilingual OCR research and applications: an overview. In: Proceedings of the 4th International Workshop on Multilingual OCR, p. 1. ACM (2013)

    Google Scholar 

  2. Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: Word-level thirteen official Indic languages database for script identification in multi-script documents. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 16–27. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4859-3_2

    Chapter  Google Scholar 

  3. Roy, K.: Document image analysis for a major Indic script Bangla - advancement and scope. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 125–134. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4859-3_12

    Chapter  Google Scholar 

  4. Pati, P.B., Ramakrishnan, A.G.: Word level multi-script identification. Pattern Recogn. Lett. 29(9), 1218–1229 (2008)

    Article  Google Scholar 

  5. Shi, B., Yao, C., Zhang, C., Guo, X., Huang, F., Bai, X.: Automatic script identification in the wild. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 531–535. IEEE (2015)

    Google Scholar 

  6. Ghosh, M., Obaidullah, S.M., Santosh, K.C., Das, N., Roy, K.: Artistic multi-character script identification using iterative isotropic dilation algorithm. In: Santosh, K.C., Hegadi, R.S. (eds.) RTIP2R 2018. CCIS, vol. 1037, pp. 49–62. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9187-3_5

    Chapter  Google Scholar 

  7. Ghosh, M., Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Das, N., Roy, K.: Artistic multi-script identification at character level with extreme learning machine. Procedia Comput. Sci. 167, 496–505 (2020)

    Article  Google Scholar 

  8. Santosh, K.C.: Complex and composite graphical symbol recognition and retrieval: a quick review. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 3–15. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4859-3_1

    Chapter  Google Scholar 

  9. Yi, C., Tian, Y.: Assistive text reading from complex background for blind persons. In: Iwamura, M., Shafait, F. (eds.) CBDAR 2011. LNCS, vol. 7139, pp. 15–28. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29364-1_2

    Chapter  Google Scholar 

  10. Yan, C., et al.: A fast Uyghur text detector for complex background images. IEEE Trans. Multimed. 20(12), 3389–3398 (2018)

    Article  Google Scholar 

  11. Obaidullah, S.M., Halder, C., Santosh, K.C., Das, N., Roy, K.: PHDIndic\(\_\)11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimed. Tools Appl. 77(2), 1643–1678 (2018)

    Article  Google Scholar 

  12. Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: Off-line handwritten character recognition of Devnagari script. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 1, pp. 496–500. IEEE (2007)

    Google Scholar 

  13. Shi, B., Bai, X., Yao, C.: Script identification in the wild via discriminative convolutional neural network. Pattern Recogn. 52, 448–458 (2016)

    Article  Google Scholar 

  14. Busch, A., Boles, W.W., Sridharan, S.: Texture for script identification. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1720–1732 (2005)

    Article  Google Scholar 

  15. Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S.: Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recogn. Lett. 34(2), 107–116 (2013)

    Article  Google Scholar 

  16. Bhunia, A.K., Konwer, A., Bhunia, A.K., Bhowmick, A., Roy, P.P., Pal, U.: Script identification in natural scene image and video frames using an attention based Convolutional-LSTM network. Pattern Recogn. 85, 172–184 (2019)

    Article  Google Scholar 

  17. Shijian, L., Tan, C.L.: Script and language identification in noisy and degraded document images. IEEE Trans. Pattern Anal. Mach. Intell. 30(1), 14–24 (2007)

    Article  Google Scholar 

  18. Lu, S., Tan, C.L., Huang, W.: Language identification in degraded and distorted document images. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 232–242. Springer, Heidelberg (2006). https://doi.org/10.1007/11669487_21

    Chapter  Google Scholar 

  19. Huang, W., Lin, Z., Yang, J., Wang, J.: Text localization in natural images using stroke feature transform and text covariance descriptors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1241–1248 (2013)

    Google Scholar 

  20. Neumann, L., Matas, J.: Text localization in real-world images using efficiently pruned exhaustive search. In: 2011 International Conference on Document Analysis and Recognition, pp. 687–691. IEEE (2011)

    Google Scholar 

  21. Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3538–3545. IEEE (2012)

    Google Scholar 

  22. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  23. Hinton, G., et al.: Deep neural networks for acoustic modelling in speech recognition. IEEE Signal Process. Mag. 29 (2012)

    Google Scholar 

  24. He, W., Zhang, X.Y., Yin, F., Liu, C.L.: Deep direct regression for multi-oriented scene text detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 745–753 (2017)

    Google Scholar 

  25. Zhang, D., Wong, A., Indrawan, M., Lu, G.: Content-based image retrieval using Gabor texture features. IEEE Trans. PAMI 13 (2000)

    Google Scholar 

  26. Soh, L.K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)

    Article  Google Scholar 

  27. Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)

    Article  Google Scholar 

  28. Jolliffe, I.T.: Principal components in regression analysis. In: Jolliffe, I.T. (ed.) Principal Component Analysis. SSS, pp. 167–198. Springer, Heidelberg (2002). https://doi.org/10.1007/0-387-22440-8_8

    Chapter  MATH  Google Scholar 

  29. 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. Photogram. Remote Sens. 67, 93–104 (2012)

    Article  Google Scholar 

  30. Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22, pp. 41–46 (2001)

    Google Scholar 

  31. Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20(15), 2429–2437 (2004)

    Article  Google Scholar 

  32. 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 

  33. Thepade, S.D., Kalbhor, M.M.: Image cataloging using Bayes, Function, Lazy, Rule, Tree classifier families with row mean of Fourier transformed image content. In: 2015 International Conference on Information Processing (ICIP), pp. 680–684. IEEE (2015)

    Google Scholar 

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Correspondence to Mridul Ghosh .

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Ghosh, M., Mukherjee, H., Roy, S.S., Obaidullah, S.M., Santosh, K.C., Roy, K. (2021). Script Identification of Movie Titles from Posters. In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-0507-9_10

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  • DOI: https://doi.org/10.1007/978-981-16-0507-9_10

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