Skip to main content

Script Identification in Natural Scene Images: A Dataset and Texture-Feature Based Performance Evaluation

  • Conference paper
  • First Online:
Proceedings of International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 460))

Abstract

Recognizing text with occlusion and perspective distortion in natural scenes is a challenging problem. In this work, we present a dataset of multi-lingual scripts and performance evaluation of script identification in this dataset using texture features. A ‘Station Signboard’ database that contains railway sign-boards written in 5 different Indic scripts is presented in this work. The images contain challenges like occlusion, perspective distortion, illumination effect, etc. We have collected a total of 500 images and corresponding ground-truths are made in semi-automatic way. Next, a script identification technique is proposed for multi-lingual scene text recognition. Considering the inherent problems in scene images, local texture features are used for feature extraction and SVM classifier, is employed for script identification. From the preliminary experiment, the performance of script identification is found to be 84 % using LBP feature with SVM classifier.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. Chanda, S., Franke, K., Pal, U.: Text independent writer identification for oriya script. In: Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on. pp. 369–373. IEEE (2012)

    Google Scholar 

  2. Ghosh, D., Dube, T., Shivaprasad, A.P.: Script recognition–a review. Pattern Analysis and Machine Intelligence, IEEE Transactions on 32(12), 2142–2161 (2010)

    Google Scholar 

  3. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern recognition 42(3), 425–436 (2009)

    Google Scholar 

  4. Murala, S., Maheshwari, R., Balasubramanian, R.: Directional local extrema patterns: a new descriptor for content based image retrieval. International Journal of Multimedia Information Retrieval 1(3), 191–203 (2012)

    Google Scholar 

  5. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on 24(7), 971–987 (2002)

    Google Scholar 

  6. Pal, U., Sinha, S., Chaudhuri, B.: Multi-script line identification from indian documents. In: Proceedings of Seventh International Conference on Document Analysis and Recognition. pp. 880–884. IEEE (2003)

    Google Scholar 

  7. Phan, 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). pp. 1240–1244. IEEE (2011)

    Google Scholar 

  8. Shi, B., Yao, C., Zhang, C., Guo, X., Huang, F., Bai, X.: Automatic script identification in the wild. In: Proceedings of ICDAR. No. 531–535 (2015)

    Google Scholar 

  9. Shijian, L., Tan, C.L.: Script and language identification in noisy and degraded document images. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(1), 14–24 (2008)

    Google Scholar 

  10. Shivakumara, P., Yuan, Z., Zhao, D., Lu, T., Tan, C.L.: New gradient-spatial-structural features for video script identification. Computer Vision and Image Understanding 130, 35–53 (2015)

    Google Scholar 

  11. Singhal, V., Navin, N., Ghosh, D.: Script-based classification of hand-written text documents in a multilingual environment. In: Proceedings of 13th International Workshop on Research Issues in Data Engineering: Multi-lingual Information Management (RIDE-MLIM). pp. 47–54. IEEE (2003)

    Google Scholar 

  12. Sun, Q.Y., Lu, Y.: Text location in scene images using visual attention model. International Journal of Pattern Recognition and Artificial Intelligence 26(04), 1–22 (2012)

    Google Scholar 

  13. Ullrich, C.: Support vector classification. In: Forecasting and Hedging in the Foreign Exchange Markets, pp. 65–82. Springer (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manisha Verma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this paper

Cite this paper

Verma, M., Sood, N., Roy, P.P., Raman, B. (2017). Script Identification in Natural Scene Images: A Dataset and Texture-Feature Based Performance Evaluation. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2107-7_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2106-0

  • Online ISBN: 978-981-10-2107-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics