Skip to main content
SpringerLink
Log in

Symbolic Approach for Word-Level Script Classification in Video Frames

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

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

  • 962 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. Pati, P.B., Ramakrishnan, A.G.: OCR in Indian scripts: a survey. J. IETE Tech. Rev. 22, 217–227 (2015)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  14. Bharath Bhushan, S.N., Danti, A.: Classification of text documents based on score level fusion approach. Pattern Recogn. Lett. 94, 118–126 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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). https://doi.org/10.1007/978-3-540-88682-2_27

    Chapter  Google Scholar 

  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 

Download references

Acknowledgment

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.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Research Foundation, Maharaja Institue of Technology, Mysore, Karnataka, India

    C. Sunil & H. K. Chethan

  2. Department of Studies in Computer Science, University of Mysore, Manasagangotri, 570006, Mysore, India

    K. S. Raghunandan & G. Hemantha Kumar

Authors
  1. C. Sunil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. S. Raghunandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. K. Chethan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Hemantha Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Sunil .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sunil, C., Raghunandan, K.S., Chethan, H.K., Kumar, G.H. (2019). Symbolic Approach for Word-Level Script Classification in Video Frames. 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_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9187-3_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation