Skip to main content
SpringerLink
Log in

Interval type-2 fuzzy set and human vision based multi-scale geometric analysis for text-graphics segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a novel method for texture-based text-graphic segmentation in a text embedded image. In the method, features are computed applying Multi-scale Geometric Analysis(MGA). The MGA of the image is done by Nonsubsampled contourlet transform(NSCT). The NSCT sub-bands help to generate the features which represent textures of the text portions and graphics portions of the image. In a segmentation process, the uncertainties arise mainly for two reasons: one is the ambiguity in gray level and other is the spatial ambiguity. Here the uncertainties are managed by interval type2 fuzzy set (IT2FS). The human vision model called human psychovisual phenomenon (HVS) is incorporated in the process for generating the interval type-2 fuzzy membership functions (IT2FMF). The efficiency of the proposed scheme is measured on the benchmark dataset. The robustness and performance bound of the proposed technique under noise corruption are measured statistically using modified Cramer-Rao bound. We found that effectiveness of the features by NSCT in combination with the IT2FS are quite promising in comparison to the state-of-the-arts methods.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Acharyya M, Kundu MK (2001) Adaptive basis selection for multitexture segmentation by m-band wavelet packet frames. Proc Int Conf Image Process 2:622–625

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

    Article  Google Scholar 

  3. Bai B, Yin F, Liu CL (2014) A seed-based segmentation method for scene text extraction. IAPR International Workshop on Document Analysis Systems, pp 262–266

  4. Bustince H, Barrenechea E, Pagola M (2006) Restricted equivalence functions. Fuzzy Sets Syst 157:2333–2346

    Article  MathSciNet  MATH  Google Scholar 

  5. Candes EJ, Donoho DL (2003) New tight frames of curvelets and optimal representations of objects with singularities. Commun Pur Appl Math 57:219–266

    Article  MathSciNet  MATH  Google Scholar 

  6. Chan W, Coghill G (2001) Text analysis using local energy. Pattern Recogn 34:2523–2532

    Article  MATH  Google Scholar 

  7. Chen D, Odobez J-M, Bourlard H (2004) Text detection and recognition in images and video frames. Pattern Recogn 37:595–608

    Article  Google Scholar 

  8. Cho H, Sung M, Jun B (2016) Canny text detector: Fast and robust scene text localization algorithm. In: International conference on computer vision and pattern recognition (CVPR), pp 3566–3573

  9. Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design and applications. IEEE Trans Image Process 15(10):3089–3101

    Article  Google Scholar 

  10. Dhar S, Kundu MK (2017) Accurate segmentation of complex document image using digital shearlet transform with neutrosophic set as uncertainty handling tool. Appl Soft Comput 61:412–426

    Article  Google Scholar 

  11. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. Proc Br Mach Vis Conf 14(12):2091–2106

    Google Scholar 

  12. Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303–338

    Article  Google Scholar 

  13. Grana C, Serra G, Manfredi M, Coppi D, Cucchiara R (2016) Layout analysis and content enrichment of digitized books. Multimed Tools Appl 75 (7):3879–3900

    Article  Google Scholar 

  14. Gomez L, Karatzas D (2013) Multi-script text extraction from natural scenes. In: Proceedings of international conference on document analysis and recognition, pp 467–471

  15. Hartnett K (2019) Foundations built for a general theory of neural networks. Quanta magazine

  16. He T, Huang W, Qiao Y, Yao J (2016) Text-attentional convolutional neural network for scene text detection. IEEE Trans Image Process 25:2529–2541

    Article  MathSciNet  MATH  Google Scholar 

  17. He P, Huang W, He T, Zhu Q, Qiao Y, Li X (2017) Single shot text detector with regional attention. In: Proceedings of international conference on computer vision (ICCV)

  18. Hosseini S, Lee SH, Cho NI (2018) Feeding hand-crafted features for enhancing the performance of convolutional neural networks. arXiv:1801.07848

  19. Huang W, Lin Z, Yang J, Wang J (2013) 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

  20. Huang W, Yu Q, Tang X (2014) Robust scene text detection with convolution neural network induced MSER trees. Proc Eur Conf Comput Vis, LNCS 8692:497–511

    Google Scholar 

  21. ICDAR2015 dataset. http://rrc.cvc.uab.es/ (2015)

  22. Kaist scene text database. www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database (2011)

  23. Karnik NN, Mendel JM (1989) Introduction to type-2 fuzzy logic systems. In: Proceeding of International Conference on Fuzzy Systems, pp 915–920

  24. Kim SH, An KJ, Jang SW, Kim GY (2016) Texture feature-based text region segmentation in social multimedia data. Multimed Tools Appl 75(20):12815–12829

    Article  Google Scholar 

  25. Kobchaisawat T, Chalidabhongse TH (2014) Thai text localization in natural scene images using convolutional neural network. Signal and Information Processing Association Annual Summit and conference (APSIPA)

  26. Kundu MK, Pal S (1986) Thresholding for edge detection using human psychovisual phenomena. Pattern Recogn Lett 4(6):433–441

    Article  Google Scholar 

  27. Kundu MK, Acharyya M (2003) M-band wavelets:application to texture segmentation for real life image analysis. Int J Wavelets, Multiresolution Inf Process 1 (1):115–119

    Article  MATH  Google Scholar 

  28. Kundu MK, Dhar S, Banerjee M (2012) A new approch for segmentation of image and text in natural and commercial text documents. In: Proceedings of international conference on communications, devices and intelligent system, pp 86–88

  29. Lan Z, Yu S-I, Yao D, Lin M, Raj B, Hauptmann A (2016) The best of both worlds: Combining data-independent and data-driven approaches for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 123–132

  30. Li Y, Lu H (2012) Scene text detection via stroke width. In: Proceedings of international conference on pattern recognition, pp 681–684

  31. Liang CW, Chen PY (2004) Dwt based text International localization. J Appl Sci Eng 2:105–116

    Google Scholar 

  32. Liu Z, Li Y, Qi X, Yang Y, Nian M, Zhang H, Xiamixiding R (2017) Method for unconstrained text detection in natural scene image. IET Computer Vision

  33. Long S, He X, Ya C (2018) Scene text detection and recognition: The deep learning era. arXiv:1811.04256

  34. Lucas SM (2005) ICDAR2005 text locating competition results. Proc Int Conf Doc Anal Recogn 1:80–84

    Google Scholar 

  35. López F, Valiente J, Baldrich R, Vanrell M (2005) Fast surface grading using color statistics in the cie lab space. In: Iberian conference on pattern recognition and image analysis, pp 666–673

  36. Maji P, Roy S (2015) Rough -fuzzy clustering and multiresolution image analysis for text-graphics segmentation. Appl Soft Comput 30:705–721

    Article  Google Scholar 

  37. Mitra P, Murty CA, Pal S (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

    Article  Google Scholar 

  38. Murthy CA, Pal S (1992) Histogram thresholding by minimizing gray level fuzziness. Inf Sci 60:107–135

    Article  MATH  Google Scholar 

  39. Nagg G, Seth S, Viswanathan M (1992) A protoptype document image analysis system for technical journals. Computer 25:10–22

    Article  Google Scholar 

  40. Nguyen DT, Pham TD, Na RB, Park KR (2018) Combining deep and handcrafted image features for presentation attack detection in face recognition systems using visible-light camera sensors. Sensors 18(3):699

    Article  Google Scholar 

  41. Park J, Lee G, Kim E, Lim J, Kim S, Yang H, Lee M, Hwang S (2010) Automatic detection and recognition of korean text in outdoor signboard images. Pattern Recogn Lett 31(12):1728–1739

    Article  Google Scholar 

  42. Pennec EL, Mallat S (2000) Image compression with geometrical wavelets. In: 2000 Proceedings. 2000 International Conference on Image Processing, vol 1, pp 661–664

  43. Peng R, Varshney PK (2015) On performance limit of image segmentation algorithms. Comput Vis Image Underst 132:24–38

    Article  Google Scholar 

  44. Rosenfield A (1998) Fuzzy geometry: an updated overview. Inf Sci 110:127–133

    Article  MathSciNet  Google Scholar 

  45. Roy S, Kundu MK, Granlund GH (1996) Uncertainty relations and time-frequency distributions for unsharp observables. Inf Sci 89:193–209

    Article  MATH  Google Scholar 

  46. Shi C, Wang C, Xiao B, Zhang Y (2013) Scene text detection using graph model built upon maximally stable extremal regions. Pattren Recogn Letter 34:107–116

    Article  Google Scholar 

  47. Szmidt E, Kacprzyk J (2001) Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst 118(3):467–477

    Article  MathSciNet  MATH  Google Scholar 

  48. Tian Z, Huang W, He T, He P, Qiao Y (2016) Detecting text in natural image with connectionist text proposal network. In: Proceedings of European conference on computer vision (ECCV)

  49. Wei Y, Zhang Z, Shen W, Zeng D, Fang M, Zhou S (2017) Text detection in scene images based on exhaustive segmentation. Signal Process Image Commun 50:1–8

    Article  Google Scholar 

  50. Yang H, Quehl B, Sack H (2014) A framework for improved video text detection and recognition. Multimed Tools Appl 69(1):217–245

    Article  Google Scholar 

  51. Yao C, Bai X, Liu W, Ma Y, Tu Z (2012) Detecting texts of arbitrary orientations in natural images. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 1083–1090

  52. Yi C, Tian YL (2011) Text string detection from natural scenes by structure-based partition and grouping. IEEE Trans Image Process 20(9):2594–2605

    Article  MathSciNet  MATH  Google Scholar 

  53. Yin X, Yin X, Hao HW, Hung K (2014) Robust text detection in natural scene images. IEEE Trans Pattern Anal Mach Intell 36:970–983

    Article  Google Scholar 

  54. Zhu A, Wang G, Dong Y (2015) Detecting natural scenes text via auto image partition, two-stage grouping and two-layer classification. Pattern Recogn Lett 67:153–162

    Article  Google Scholar 

  55. Zhu Y, Yao C, Bai X (2016) Scene text detection and recognition:recent advances and future trends. Front Comput Sci 10:19–36

    Article  Google Scholar 

  56. Zhu W, Chen Q, Wei C, Li Z (2017) A segmentation algorithm based on image projection for complex text layout. In: AIP Conference Proceedings. AIP Publishing, vol 1890, pp 030011

Download references

Author information

Authors and Affiliations

  1. RCCIIT, Beliaghata, Kolkata, 700010, India

    Soumyadip Dhar

  2. ISI, B.T. Road, Kolkata, 700108, India

    Malay K. Kundu

Authors
  1. Soumyadip Dhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malay K. Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumyadip Dhar.

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

Dhar, S., Kundu, M.K. Interval type-2 fuzzy set and human vision based multi-scale geometric analysis for text-graphics segmentation. Multimed Tools Appl 78, 22939–22957 (2019). https://doi.org/10.1007/s11042-019-7649-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7649-6

Keywords

Search

Navigation