Skip to main content
SpringerLink
Log in

Text and graphics segmentation of newspapers printed in Gurmukhi script: a hybrid approach

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Newspapers are always a standard medium to convey important information to masses of people in recent time as well as in old time. An automated system is required to convert information into a processable form so that information could be searchable. Many efforts have been done on Gurmukhi script documents in typed or written form, but very few articles are present on Gurmukhi script newspaper text recognition or text and image segmentation. Image/graphics segmentation of text is mandatory before feeding newspaper text to OCR for accurate results. In the literature, many techniques have been proposed for segmenting images and text, but many are complex in nature. In this article, the authors have proposed a very simple and effective hybrid approach based on run length smoothing algorithm and projection profile to segment an image from text in Gurmukhi script newspaper articles. Both horizontal and vertical run length smearing algorithm is used for labeling the regions. Logical AND operator is applied to resultant images to identify the text and image regions. To segment the image region among the labeled regions, projection profile technique is implemented. The combination of these two techniques has produced very good results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Antonacopoulos, A., Pletschacher, S., Bridson, D., Papadopoulos, C.: ICDAR 2009 page segmentation competition. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1370–1374 (2009)

  2. Hadjar, K., Hitz, O., Ingold, R.: Newspaper page decomposition using a split and merge approach. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, pp. 1186–1189 (2001)

  3. Wong, K.Y., Casey, R.G., Wahl, F.M.: Document analysis system. IBM J. Res. Dev. 26(6), 647–656 (1982)

    Article  Google Scholar 

  4. Mao, S., Rosenfeld, A., Kanungo, T.: Document structure analysis algorithms: a literature survey. Doc. Recogn. Retriev. 5010, 197–208 (2003)

    Google Scholar 

  5. Mitchell, P.E., Yan, H.: Newspaper document analysis featuring connected line segmentation. In: Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, vol. 11, pp. 77–81 (2001)

  6. Caponetti, L., Castiello, C., Górecki, P.: Document page segmentation using neuro-fuzzy approach. Appl. Soft Comput. 8(1), 118–126 (2008)

    Article  Google Scholar 

  7. Wu, V., Manmatha, R., Riseman, E.M.: Textfinder: an automatic system to detect and recognize text in images. IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1224–1229 (1999)

    Article  Google Scholar 

  8. Jain, A.K., Bhattacharjee, S.K., Chen, Y.: On texture in document images. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 677–680 (1992)

  9. Gatos, B., Mantzaris, S.L., Chandrinos, K.V., Tsigris, A., Perantonis, S.J.: Integrated algorithms for newspaper page decomposition and article tracking. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, pp. 559–562 (1999)

  10. Lin, M.W., Tapamo, J.R., Ndovie, B.: A texture-based method for document segmentation and classification. South Afr. Comput. J. 36, 49–56 (2006)

    Google Scholar 

  11. Sun, H.M.: Enhanced constrained run-length algorithm for complex layout document processing. Int. J. Appl. Sci. Eng. 4(3), 297–309 (2006)

    Google Scholar 

  12. Grover, S., Arora, K., Mitra, S.K.: Text extraction from document images using edge information. In: Proceedings of IEEE India Conference (INDICON), pp. 1–4 (2009)

  13. Gupta, P., Vohra, N., Chaudhury, S., Joshi, S.D.: Wavelet based page segmentation. In: Proceedings of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp. 51–56 (2000)

  14. Rege, P.P., Chandrakar, C.A.: Text-image separation in document images using boundary/perimeter detection. ACEEE Int. J. Signal Image Process. 3(1), 10–14 (2012)

    Google Scholar 

  15. Aparna, K.H., Jaganathan, S., Krishnan, P., Chakravarthy, V.S.: An optical character recognition system for Tamil newsprint. In: Proceedings of International Conference on Universal Knowledge and Language, pp. 881–886 (2002)

  16. Kaur, R.P., Jindal, M.K.: Problems in making OCR of Gurmukhi script newspapers. Int. J. Adv. Res. Comput. Sci. 7(6), 16–22 (2016)

    Google Scholar 

  17. Khedekar, S., Ramanaprasad, V., Setlur, S., Govindaraju, V.: Text-image separation in Devanagari documents. In: Proceedings of Seventh International Conference on Document Analysis and Recognition, pp. 1265–1269 (2003)

  18. Kaur, R.P., Jindal, M.K.: A survey on newspaper image segmentation techniques. Int. J. Adv. Res. Sci. Eng. 6(10), 1789–1797 (2017)

    Google Scholar 

  19. Kumar, S.S., Rajendran, P., Prabaharan, P., Soman, K.P.: Text/image region separation for document layout detection of old document images using non-linear diffusion and level set. Proc. Comput. Sci. 93, 469–477 (2016)

    Article  Google Scholar 

  20. Kumar, M., Sharma, R.K., Kumar, M.K.: Offline handwritten Gurmukhi script recognition. Ph.D. Thesis, Thapar University, Patiala, Punjab, India (2015)

  21. Wu, H.Y., Kornprobst, P.: Multilayered Analysis of Newspaper Structure and Design (2019)

  22. Safonov, I.V., Kurilin, I.V., Rychagov, M.N., Tolstaya, E.V.: Segmentation of scanned images of newspapers and magazines. In: Document Image Processing for Scanning and Printing. Springer, pp. 107–122 (2019)

  23. Barman, R., Ehrmann, M., Clematide, S., Oliveira, S.A., Kaplan, F.: Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers. arXiv preprint arXiv:2002 (2020)

  24. Su, B., Lu, S., Tan, C.L.: Robust document image binarization technique for degraded document images. IEEE Trans. Image Process. 22(4), 1408–1417 (2012)

    MathSciNet  MATH  Google Scholar 

  25. Acharyya, M., Kundu, M.K.: Document image segmentation using wavelet scale-space features. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1117–1127 (2002)

    Article  Google Scholar 

  26. Deng, S., Latifi, S., Regentova, E.: Document segmentation using polynomial spline wavelets. Pattern Recogn. 34(12), 2533–2545 (2001)

    Article  Google Scholar 

  27. Lee, G.B., Odoyo, W.O., Lee, J.H., Chung, Y., Cho, B.J.: Two texture segmentation of document image using wavelet packet analysis. In: Proceedings of the 9th International Conference on Advanced Communication Technology, pp. 395–398 (2007)

  28. Haneda, E., Bouman, C.A.: Text segmentation for MRC document compression. IEEE Trans. Image Process. 20(6), 1611–1626 (2011)

    Article  MathSciNet  Google Scholar 

  29. Bouressace, H., Csirik, J.: A convolutional neural network for Arabic document analysis. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1–6 (2019)

  30. Almutairi, A., Almashan, M.: Instance segmentation of newspaper elements using mask R-CNN. In: 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1371–1375 (2019)

  31. Roy, S.: A Study on Multiresolution Image Analysis and Rough-Fuzzy Clustering for Text-Graphics Segmentation. Elsevier Science, Amsterdam (2015)

    Google Scholar 

  32. Fan, K.C., Liu, C.H., Wang, Y.K.: Segmentation and classification of mixed text/graphics/image documents. Pattern Recogn. Lett. 15(12), 1201–1209 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Guru Nanak College, Muktsar, Punjab, India

    Rupinder Pal Kaur

  2. Department of Computer Science and Applications, Panjab University Regional Centre, Muktsar, Punjab, India

    M. K. Jindal

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

Authors
  1. Rupinder Pal Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. K. Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, R.P., Jindal, M.K. & Kumar, M. Text and graphics segmentation of newspapers printed in Gurmukhi script: a hybrid approach. Vis Comput 37, 1637–1659 (2021). https://doi.org/10.1007/s00371-020-01927-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01927-0

Keywords

Search

Navigation