Symbolic Approach for Word-Level Script Classification in Video Frames

  • C. SunilEmail author
  • K. S. Raghunandan
  • H. K. Chethan
  • G. Hemantha Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


In recent years, addiction towards internet and digital world has made difficult for people to understand multilingual scripts in various circumstances. In this work, we proposed a model for classification of South Indian multilingual word script extracted from video frames namely, Kannada, Tamil, Telugu, Malayalam and English. Firstly, we extracted Local Binary Pattern (LBP), Histogram of Oriented Gradients (HoG) and Gradient Local Auto-Correlation (GLAC) features for each multilingual word script. The multilingual word script consists of five classes and each class of images are clustered by implementing k-means clustering technique. Further, we proposed symbolic representation to capture intra-class variations between each clusters and symbolic classifier is employed for classification. For experimentation, we have extracted 600 word images from each script and total of 3000 word images from video frames. Further, we have made comparative study to show the robustness of symbolic representation and classifier with SVM and ANN classifiers.


Multilingual scripts identification Interval data K-means clustering Symbolic representation Symbolic classification 



The work done in this paper was supported by High Performance Computing Lab, under UPE Grant Department of Studies in Computer Science, University of Mysore, Mysore.


  1. 1.
    Ubul, K., Tursun, G., Aysa, A., Impedovo, D., Pirlo, G., Yibulayin, T.: Script identification of multi-script documents. IEEE Access 5, 6546–6559 (2017)Google Scholar
  2. 2.
    Pati, P.B., Ramakrishnan, A.G.: OCR in Indian scripts: a survey. J. IETE Tech. Rev. 22, 217–227 (2015)CrossRefGoogle Scholar
  3. 3.
    Pan, T.Q., Shivakumara, P., Ding, Z., Lu, S., Tan, C.L.: Video script identification based on text lines. In: International Conference on Document Analysis and Recognition (ICDAR 2011). IEEE (2011)Google Scholar
  4. 4.
    Rani, R., Dhir, R., Lehal, G.S.: Script identification of pre-segmented multi-font characters and digits. In: International Conference on Document Analysis and Recognition (ICDAR 2013). IEEE (2013)Google Scholar
  5. 5.
    Pal, U., Sharma, N., Wakabayashi, T., kimura, F.: Handwritten numerical recognition of six popular Indian scripts. In: International Conference on document Analysis and Recognition (ICDAR 2007). IEEE (2007)Google Scholar
  6. 6.
    Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: Word level script identification from Bangla and Devanagari handwritten texts mixed with roman scripts. J. Comput. 2, 103–108 (2010)Google Scholar
  7. 7.
    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, 87–106 (2017)CrossRefGoogle Scholar
  8. 8.
    Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: PHDIndic\(\_\)11: page level handwritten document image dataset of 11 official Indic scripts for script identification. Multimedia Tools Appl. 77, 1643–1678 (2017)CrossRefGoogle Scholar
  9. 9.
    Obaidullah, S.M., Goswami, C., Santosh, K.C., Halder, C., Das, N., Roy, K.: Separating Indic Scripts with matra for effective handwritten script identification in multi-scripts documents. Int. J. Pattern Recognit. Artif. Intell. 31(5), 1753003 (2017)CrossRefGoogle Scholar
  10. 10.
    Obaidullah, S.M., 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. 7(2), 81–94 (2017)CrossRefGoogle Scholar
  11. 11.
    Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: Handwritten Indic script identification in multi-script images: a survey. Int. J. Pattern Recognit. Artif. Intell. 32(10), 1856012 (2018)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Gomez, L., Nicolau, A., Karatzas, D.: Improving patch-based scene text script identification with ensembles of conjoined networks. Pattern Recogn. 67, 85–96 (2017)CrossRefGoogle Scholar
  14. 14.
    Bharath Bhushan, S.N., Danti, A.: Classification of text documents based on score level fusion approach. Pattern Recogn. Lett. 94, 118–126 (2017)CrossRefGoogle Scholar
  15. 15.
    Shi, B., Bai, X., Yao, C.: Script identification in the wild via discriminative convolution neural network. Pattern Recogn. 52, 448–458 (2016)CrossRefGoogle Scholar
  16. 16.
    Jamil, A., Batool, A., Malik, Z., Mizar, A., Siddiqi, I.: Multilingual artificial text extraction and script identification from video images. Int. J. Adv. Comput. Sci. Appl. 7(4) (2016)Google Scholar
  17. 17.
    Singh, P.K., Sarkar, R., Nasipuri, M., Doermann, D.: Word-level script identification for handwritten Indic scripts. In: International Conference on Document Analysis and Recognition (ICDAR 2015) (2015)Google Scholar
  18. 18.
    Angadi, S.A., Kodabagi, M.M.: A fuzzy approach for word level script identification of text in low resolution display board images using wavelet features. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI 2013). IEEE (2013)Google Scholar
  19. 19.
    Ojala, T., Pietikainen, M.: Multiresolution gray-scale invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–981 (2002)CrossRefGoogle Scholar
  20. 20.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)Google Scholar
  21. 21.
    Kobayashi, T., Otsu, N.: Image feature extraction using gradient local auto-correlations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 346–358. Springer, Heidelberg (2008). Scholar
  22. 22.
    Guru, D.S., Vinay Kumar, N.: Symbolic representation and classification of logos. In: Proceedings of International Conference on Computer Vision and Image Processing (CVIP 2016) (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • C. Sunil
    • 1
    Email author
  • K. S. Raghunandan
    • 2
  • H. K. Chethan
    • 1
  • G. Hemantha Kumar
    • 2
  1. 1.Department of Computer Science and Engineering, Maharaja Research FoundationMaharaja Institue of TechnologyMysoreIndia
  2. 2.Department of Studies in Computer ScienceUniversity of MysoreManasagangotriIndia

Personalised recommendations