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Fuzzy Techniques for Text Localisation in Images

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 96))

Text information extraction represents a fundamental issue in the context of digital image processing. Inside this wide area of research, a number of specific tasks can be identified ranging from text detection to text recognition. In this chapter, we deal with the particular problem of text localisation, which aims at determining the exact location where the text is situated inside a document image. The strict connection between text localisation and image segmentation is highlighted in the chapter and a review of methods for image segmentation is proposed. Particularly, the benefits coming from the employment of fuzzy and neuro-fuzzy techniques in this field is assessed, thus indicating a way to combine Computational Intelligence methods and document image analysis. Three peculiar methods based on image segmentation are presented to show different applications of fuzzy and neuro-fuzzy techniques in the context of text localisation.

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References

  1. Colombo C, Del Bimbo A, Pala P (1999) IEEE Multimedia 6(3):38–53

    Article  Google Scholar 

  2. Long F, Zhang H, Feng D (2003) Fundamentals of content-based image retr- ieval, in: Feng D ZHE Siu WC (ed.) Multimedia information retrieval and management - technological fundamentals and applications. Springer, Berlin Heidelberg New York

    Google Scholar 

  3. Yang M, Kriegman D, Ahuja N (2002) IEEE Trans Pattern Anal Mach Intell 24(1):34–58

    Article  Google Scholar 

  4. Dingli A, Ciravegna F, Wilks Y (2003) Automatic semantic annotation using unsupervised information extraction and integration, in: Proceedings of semAnnot workshop

    Google Scholar 

  5. Djioua B, Flores JG, Blais A, Desclés JP, Guibert G, Jackiewicz A, Priol FL, Nait-Baha L, Sauzay B (2006) EXCOM: An automatic annotation Engine for semantic information, in: Proceedings of FLAIRS conference, pp. 285–290

    Google Scholar 

  6. Orasan C (2005) Automatic annotation of corpora for text summarisation: A comparative study, in: Computational linguistics and intelligent text processing, volume 3406/2005, Springer, Berlin Heidelberg New York

    Google Scholar 

  7. Karatzas D, Antonacopoulos A (2003) Two Approaches for Text Segmentation in Web Images, in: Proceedings of the 7th International Conference on Document Analsis and Recognition (ICDAR2003), IEEE Computer Society Press, Cambridge, UK pp. 131–136

    Chapter  Google Scholar 

  8. Jung K, Kim K, Jain A (2004) Pattern Recognit 37:977–997

    Article  Google Scholar 

  9. Chen D, Odobez J, Bourlard H (2002) Text segmentation and recognition in complex background based on Markov random field, in: Proceedings of International Conference on Pattern Recognition, pp. 227–230

    Google Scholar 

  10. Li H, Doerman D, Kia O (2000) IEEE Trans Image Process 9(1):147–156

    Article  Google Scholar 

  11. Li H, Doermann D (2000) Superresolution-based enhancement of text in digital video, in: Proceedings of International Conference of Pattern Recognition, pp. 847–850

    Google Scholar 

  12. Li H, Kia O, Doermann D (1999) Text enhancement in digital video, in: Proceedings of SPIE, Document Recognition IV, pp. 1–8

    Google Scholar 

  13. Sato T, Kanade T, Hughes E, Smith M (1998) Video OCR for digital news archive, in: Proceedings of IEEE Workshop on Content based Access of Image and Video Databases, pp. 52–60

    Google Scholar 

  14. Zhou J, Lopresti D, Lei Z (1997) OCR for world wide web images, in: Proceedings of SPIE on Document Recognition IV, pp. 58–66

    Google Scholar 

  15. Zhou J, Lopresti D, Tasdizen T (1998) Finding text in color images, in: Proceedings of SPIE on Document Recognition V, pp. 130–140

    Google Scholar 

  16. Ching-Yu Y, Tsai WH (2000) Signal Process.: Image Commun. 15(9):781–797

    Article  Google Scholar 

  17. Deng S, Lati S, Regentova E (2001) Document segmentation using polynomial spline wavelets, Pattern Recognition 34:2533–2545

    Article  MATH  Google Scholar 

  18. Lu Y, Shridhar M (1996) Character segmentation in handwritten words, J. of, Pattern Recognit 29(1):77–96

    Google Scholar 

  19. Mital D, Leng GW (1995) J Microcomput Appl 18(4):375–392

    Article  Google Scholar 

  20. Rossant F (2002) Pattern Recognit Lett 23(10):1129–1141

    Article  MATH  Google Scholar 

  21. Xiao Y, Yan H (2003) Text extraction in document images based on Delaunay triangulation, Pattern Recognition 36(3):799–809

    Article  MATH  MathSciNet  Google Scholar 

  22. Pratt W (2001) Digital image processing (3rd edition). Wiley, New York, NY

    Google Scholar 

  23. Haralick R (1979) Proc IEEE 67:786–804

    Article  Google Scholar 

  24. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification, IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  25. Baird H, Jones S, Fortune S (1990) Image segmentation by shape-directed covers, in: Proceedings of International Conference on Pattern Recognition, pp. 820–825

    Google Scholar 

  26. Nagy G, Seth S, Viswanathan M (1992) Method of searching and extracting text information from drawings, Computer 25:10–22

    Article  Google Scholar 

  27. O’Gorman L (1993) IEEE Trans Pattern Anal Mach Intell 15:1162–1173

    Article  Google Scholar 

  28. Kose K, Sato A, Iwata M (1998) Comput Vis Image Underst 70:370–382

    Article  Google Scholar 

  29. Wahl F, Wong K, Casey R (1982) Graph Models Image Process 20:375–390

    Google Scholar 

  30. Jain A, Yu B (1998) IEEE Trans Pattern Anal Mach Intell 20:294–308

    Article  Google Scholar 

  31. Pavlidis T, Zhou J (1992) Graph Models Image Process 54:484–496

    Article  Google Scholar 

  32. Hadjar K, Hitz O, Ingold R (2001) Newspaper Page Decomposition Using a Split and Merge Approach, in: Proceedings of Sixth International Conference on Document Analysis and Recognition

    Google Scholar 

  33. Jiming L, Tang Y, Suen C (1997) Pattern Recognit 30(8):1265–1278

    Article  Google Scholar 

  34. Rosenfeld A, la Torre PD (1983) IEEE Trans Syst Man Cybern SMC-13:231–235

    Google Scholar 

  35. Sahasrabudhe S, Gupta K (1992) Comput Vis Image Underst 56:55–65

    Google Scholar 

  36. Sezan M (1985) Graph Models Image Process 29:47–59

    Article  Google Scholar 

  37. Yanni M, Horne E (1994) A new approach to dynamic thresholding, in: Proceedings of EUSIPCO’94: 9th European Conference on Signal Processing 1, pp. 34–44

    Google Scholar 

  38. Sezgin M, Sankur B (2004) J Electron Imaging 13(1):146–165

    Article  Google Scholar 

  39. Kamel M, Zhao A (1993) Graph Models Image Process 55(3):203–217

    Article  Google Scholar 

  40. Solihin Y, Leedham C (1999) Integral ratio: A new class of global thresholding techniques for handwriting images, in: IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-21, pp. 761–768

    Google Scholar 

  41. Trier O, Jain A (1995) Goal-directed evaluation of binarization methods, in: IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-17, pp. 1191–1201

    Google Scholar 

  42. Bow ST (2002) Pattern Recognition and Image Preprocessing 2nd edition. Dekker, New York, NY

    Google Scholar 

  43. Jung K, Han J (2004) Pattern Recognit Lett 25(6):679–699

    Article  Google Scholar 

  44. Ohya J, Shio A, Akamatsu S (1994) IEEE Trans Pattern Anal Mach Intell 16(2):214–224

    Article  Google Scholar 

  45. Wu S, Amin A (2003) Proceedings of Seventh international conference on Document Analysis and Recognition, volume 1, pp. 493–497

    Article  Google Scholar 

  46. Canny J (1986) IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  47. Chen D, Shearer K, Bourlard H (2001) Text enhancement with asymmetric filter for video OCR, in: Proceedings of International Conference on Image Analysis and Processing, pp. 192–197

    Google Scholar 

  48. Hasan Y, Karam L (2000) IEEE Trans Image Process 9(11):1978–1983

    Article  Google Scholar 

  49. Lee SW, Lee DJ, Park HS (1996) IEEE Trans Pattern Recogn Mach Intell 18(10):1045–1050

    Article  Google Scholar 

  50. Grigorescu SE, Petkov N, Kruizinga P (2002) IEEE Trans Image Process 11(10):1160–1167

    Article  MathSciNet  Google Scholar 

  51. Livens S, Scheunders P, van de Wouwer G, Van Dyck D (1997) Wavelets for texture analysis, an overview, in: Proceedings of the Sixth International Conference on Image Processing and Its Applications, pp. 581–585

    Google Scholar 

  52. Tuceryan M, Jain AK (1998) Texture analysis, in: Chen CH, Pau LF, Wang PSP (eds.) The Handbook of Pattern Recognition and Computer Vision 2nd edition, World Scientific Publishing, River Edge, NJ pp. 207–248

    Google Scholar 

  53. Jain A, Bhattacharjee S (1992) Mach Vision Appl 5:169–184

    Article  Google Scholar 

  54. Acharyya M, Kundu M (2002) IEEE Trans Circ Syst video Technol 12(12): 1117–1127

    Article  Google Scholar 

  55. Etemad K, Doermann D, Chellappa R (1997) IEEE Trans Pattern Anal Mach Intell 19(1):92–96

    Article  Google Scholar 

  56. Mao W, Chung F, Lanm K, Siu W (2002) Hybrid Chinese/English text detection in images and video frames, in: Proceedings of International Conference on Pattern recognition, volume 3, pp. 1015–1018

    Google Scholar 

  57. Coifman R, Wickerhauser V (1992) IEEE Trans Inf Theory 38(2):713–718

    Article  MATH  Google Scholar 

  58. Coifman RR (1990) Wavelet Analysis and Signal Processing, in: Auslander L, Kailath T, Mitter SK (eds.) Signal Processing, Part I: Signal Processing Theory, Springer, Berlin Heidelberg New York, pp. 59–68, URL {citeseer.is-t}.psu.edu/coifman92wavelet.html

  59. Daubechies I (1992) Ten Lectures on Wavelets (CBMS - NSF Regional Conference Series in Applied Mathematics), Soc for Industrial & Applied Math

    Google Scholar 

  60. Bruce A, Gao HY (1996) Applied Wavelet Analysis with S-Plus, Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  61. Mallat SG (1989) IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  62. Engelbrecht A (2003) Computational Intelligence: An Introduction, WileyNew York, NY

    Google Scholar 

  63. Sincak P, Vascak J (eds.) (2000) Quo vadis computational intelligence?, Physica-Verlag

    Google Scholar 

  64. Zadeh L (1965) Inform Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  65. Klir G, Yuan B (eds.) (1996) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh, World Scientific Publishing, River Edge, NJ

    Google Scholar 

  66. Pham T, Chen G (eds.) (2000) Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems, CRC , Boca Raton, FL

    Google Scholar 

  67. Jawahar C, Ray A (1996) IEEE Signal Process Lett 3(8):225–227

    Article  Google Scholar 

  68. Jin Y (2003) Advanced Fuzzy Systems Design and Applications, Physica/ Springer, Heidelberg

    MATH  Google Scholar 

  69. Mamdani E, Assilian S (1975) Int J Man-Mach Studies 7(1):1–13

    Article  MATH  Google Scholar 

  70. Sugeno M, Kang G (1988) Structure identification of fuzzy model, Fuzzy Sets Syst 28:15–33

    Article  MATH  MathSciNet  Google Scholar 

  71. Dubois D, Prade H (1996) Fuzzy Sets Syst 84:169–185

    Article  MATH  MathSciNet  Google Scholar 

  72. Leekwijck W, Kerre E (1999) Fuzzy Sets Syst 108(2):159–178

    Article  MATH  Google Scholar 

  73. Dunn J (1974) J Cybern 3:32–57

    Article  MathSciNet  Google Scholar 

  74. Bezdek J (1981) Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition), Springer, Berlin Heidelberg New York URL http://www.amazon.co.uk/exec/obidos/ASIN/0306406713/citeulike-21

  75. Macqueen J (1967) Some methods of classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathemtical Statistics and Probability, pp. 281–297

    Google Scholar 

  76. Pham D (2001) Comput Vis Image Underst 84:285–297

    Article  MATH  MathSciNet  Google Scholar 

  77. Bezdek J, Hall L, Clarke L (1993) Med Phys 20:1033–1048

    Article  Google Scholar 

  78. Rignot E, Chellappa R, Dubois P (1992) IEEE Trans Geosci Remote Sensing 30(4):697–705

    Article  Google Scholar 

  79. Jang JS, Sun C (1995) Proc of the IEEE 83:378–406

    Article  Google Scholar 

  80. Kosko B (1991) Neural networks and fuzzy systems: a dynamical systems approach to machinhe intelligence, Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  81. Lin C, Lee C (1996) Neural fuzzy systems: a neural fuzzy synergism to intelligent systems, Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  82. Mitra S, Hayashi Y (2000) IEEE Trans Neural Netw 11(3):748–768

    Article  Google Scholar 

  83. Nauck D (1997) Neuro-Fuzzy Systems: Review and Prospects, in: Proc. Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT’97), pp. 1044–1053

    Google Scholar 

  84. Fuller R (2000) Introduction to Neuro-Fuzzy Systems, Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  85. Castellano G, Castiello C, Fanelli A, Mencar C (2005) Fuzzy Sets Syst 149(1):187–207

    Article  MATH  MathSciNet  Google Scholar 

  86. Castiello C, Gorecki P, Caponetti L (2005) Neuro-Fuzzy Analysis of Document Images by the KERNEL System, Lecture Notes in Artificial Intelligence 3849:369–374

    Google Scholar 

  87. Caponetti L, Castiello C, Gorecki P (2007) Document Page Segmentation using Neuro-Fuzzy Approach, to appear in Applied Soft Computing Journal

    Google Scholar 

  88. Gorecki P, Caponetti L, Castiello C (2006) Multiscale Page Segmentation using Wavelet Packet Analysis, in: Abstracts of VII Congress Italian Society for Applied and Industrial Mathematics (SIMAI 2006), p. 210

    Google Scholar 

  89. of Oulu Finland U, Document Image Database, http://www.ee.oulu.fi/research/imag/document/

  90. Hinds S, Fisher J, D’Amato D (1990) A document skew detection method using run-length encoding and Hough transform, in: Proc. of the 10th Int. Conference on Pattern Recognition (ICPR), pp. 464–468

    Google Scholar 

  91. Hough P (1959) Machine Analysis of Bubble Chamber Pictures, in: International Conference on High Energy Accelerators and Instrumentation, CERN

    Google Scholar 

  92. Srihari S, Govindaraju V (1989) Mach Vision Appl 2:141–153

    Article  Google Scholar 

  93. Gonzalez R, Woods R (2007) Digital Image Processing 3rd edition, Prentice Hall

    Google Scholar 

  94. Lindeberg T (1994) Scale-space theory in computer vision, Kluwer, Boston

    Google Scholar 

  95. Watt A, Policarpo F (1998) The Computer Image, ACM, Addison-Wesley

    Google Scholar 

  96. Sammon J (1970) IEEE Trans Comput C-19:826–829

    Google Scholar 

  97. Holland J (1992) Adaptation in Natural and Artificial Systems reprint edition, MIT, Cambridge, MA,

    Google Scholar 

  98. Mitchell M (1996) An Introduction to Genetic Algorithms, MIT, iSBN:0-262-13316-4

    Google Scholar 

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Górecki, P., Caponetti, L., Castiello, C. (2008). Fuzzy Techniques for Text Localisation in Images. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_10

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  • DOI: https://doi.org/10.1007/978-3-540-76827-2_10

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