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Digital Libraries and Document Image Retrieval Techniques: A Survey

  • Simone Marinai
  • Beatrice Miotti
  • Giovanni Soda
Part of the Studies in Computational Intelligence book series (SCI, volume 375)

Abstract

Nowadays, Digital Libraries have become a widely used service to store and share both digital born documents and digital versions of works stored by traditional libraries. Document images are intrinsically non-structured and the structure and semantic of the digitized documents is in most part lost during the conversion. Several techniques related to the Document Image Analysis research area have been proposed in the past to deal with document image retrieval applications. In this chapter a survey about the more recent techniques applied in the field of recognition and retrieval of text and graphical documents is presented. In particular we describe techniques related to recognition-free approaches.

Keywords

IEEE Computer Society Digital Library Document Image Dynamic Time Warping Scale Invariant Feature Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alajlan, N., Kamel, M.S., Freeman, G.H.: Geometry-based image retrieval in binary image databases. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(6), 1003–1013 (2008)CrossRefGoogle Scholar
  2. 2.
    Bai, S., Li, L., Tan, C.: Keyword spotting in document images through word shape coding. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 331–335. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  3. 3.
    Balasubramanian, A., Meshesha, M., Jawahar, C.: Retrieval from document image collections. In: Proc. IAPR Int’l Workshop on Document Analysis Systems, pp. 1–12 (2006)Google Scholar
  4. 4.
    Banerjee, S., Harit, G., Chaudhury, S.: Word image based latent semantic indexing for conceptual querying in document image databases. In: Proc. Int’l Conf. on Document Analysis and Recognition, vol. 2, pp. 1208–1212. IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  5. 5.
    Barbu, E., Héroux, P., Adam, S., Trupin, É.: Using bags of symbols for automatic indexing of graphical document image databases. In: Proc. Int’l Workshop on Graphics Recognition, pp. 195–205 (2005)Google Scholar
  6. 6.
    Belaid, A., Turcan, I., Pierrel, J.M., Belaid, Y., Hadjamar, Y., Hadjamar, H.: Automatic indexing and reformulation of ancient dictionaries. In: Proc. Int’l Workshop on Document Image Analysis for Libraries, pp. 342–354. IEEE Computer Society Press, Washington, DC, USA (2004)CrossRefGoogle Scholar
  7. 7.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar
  8. 8.
    Cao, H., Bhardwaj, A., Govindaraju, V.: Journal of Pattern Recognition 42(12), 3374 Google Scholar
  9. 9.
    Cao, H., Govindaraju, V.: Vector model based indexing and retrieval of handwritten medical forms. In: Proc. Int’l Conf. on Document Analysis and Recognition, vol. 1, pp. 88–92 (2007)Google Scholar
  10. 10.
    Chellapilla, K., Piatt, J.: Redundant bit vectors for robust indexing and retrieval of electronic ink. In: Proc. Int’l Conf. on Document Analysis and Recognition, vol. 1, pp. 387–391 (2007)Google Scholar
  11. 11.
    Choisy, C.: Dynamic handwritten keyword spotting based on the NSHP-HMM. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 242–246. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  12. 12.
    Curtis, J.D., Chen, E.: Keyword spotting via word shape recognition. In: Proc. SPIE - Document Recognition II, pp. 270–277 (1995)Google Scholar
  13. 13.
    Delalandre, M., Ogier, J.-M., Lladós, J.: A fast CBIR system of old ornamental letter. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 135–144. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Doermann, D., Doermann, D.: The indexing and retrieval of document images: A survey. Computer Vision and Image Understanding 70, 287–298 (1998)CrossRefGoogle Scholar
  15. 15.
    Fataicha, Y., Cheriet, M., Nie, Y., Suen, Y.: Retrieving poorly degraded OCR documents. International Journal of Document Analysis and Recognition 8(1), 1–9 (2006)CrossRefGoogle Scholar
  16. 16.
    Fonseca, M.J., Ferreira, A., Jorge, J.A.: Generic shape classification for retrieval. In: Proc. Int’l Workshop on Graphics Recognition, pp. 291–299 (2005)Google Scholar
  17. 17.
    Gatos, B., Pratikakis, I.: Segmentation-free word spotting in historical printed documents. In: Proc. Int’l Conf. on Document Analysis and Recognition, p. 271. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  18. 18.
    Gordo, A., Valveny, E.: A rotation invariant page layout descriptor for document classification and retrieval. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 481–485. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  19. 19.
    Govindaraju, V., Cao, H., Bhardwaj, A.: Handwritten document retrieval strategies. In: Proc. of Workshop on Analytics for Noisy Unstructured Text Data, pp. 3–7. ACM, New York (2009)CrossRefGoogle Scholar
  20. 20.
    Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)Google Scholar
  21. 21.
    Jain, A.K., Namboodiri, A.M.: Indexing and retrieval of on-line handwritten documents. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 655–659. IEEE Computer Society Press, Washington, DC, USA (2003)CrossRefGoogle Scholar
  22. 22.
    Hu, J., Kashi, R., Wilfong, G.: Comparison and classification of documents based on layout similarity. Information Retrieval 2(2/3), 227–243 (2000)CrossRefGoogle Scholar
  23. 23.
    Jones, G., Foote, J., Sparck Jones, K., Young, S.: Video mail retrieval: the effect of word spotting accuracy on precision. In: Int’l Conf. on Acoustics, Speech, and Signal Processing, vol. 1, pp. 309–312 (1995)Google Scholar
  24. 24.
    Journet, N., Ramel, J.Y., Mullot, R., Eglin, V.: A proposition of retrieval tools for historical document images libraries. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 1053–1057. IEEE Computer Society, Washington, DC, USA (2007)Google Scholar
  25. 25.
    Joutel, G., Eglin, V., Bres, S., Emptoz, H.: Curvelets based queries for CBIR application in handwriting collections. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 649–653. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  26. 26.
    Karray, A., Ogier, J.M., Kanoun, S., Alimi, M.A.: An ancient graphic documents indexing method based on spatial similarity. In: Proc. Int’l Workshop on Graphics Recognition, pp. 126–134. Springer, Heidelberg (2008)Google Scholar
  27. 27.
    Kesidis, A., Galiotou, E., Gatos, B., Lampropoulos, A., Pratikakis, I., Manolessou, I., Ralli, A.: Accessing the content of greek historical documents. In: Proc. of Workshop on Analytics for Noisy Unstructured Text Data, pp. 55–62. ACM, New York (2009)CrossRefGoogle Scholar
  28. 28.
    Khurshid, K., Faure, C., Vincent, N.: Fusion of word spotting and spatial information for figure caption retrieval in historical document images. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 266–270. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  29. 29.
    Kise, K., Wuotang, Y., Matsumoto, K.: Document image retrieval based on 2D density distributions of terms with pseudo relevance feedback. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 488–492. IEEE Computer Society Press, Washington, DC, USA (2003)CrossRefGoogle Scholar
  30. 30.
    Kogler, M., Lux, M.: Bag of visual words revisited: an exploratory study on robust image retrieval exploiting fuzzy codebooks. In: Proc. Int’l Workshop on Multimedia Data Mining, MDMKDD 2010, pp. 3:1–3:6. ACM, USA (2010)Google Scholar
  31. 31.
    Konidaris, T., Gatos, B., Ntzios, K., Pratikakis, I., Theodoridis, S., Perantonis, S.J.: Keyword-guided word spotting in historical printed documents using synthetic data and user feedback. International Journal of Document Analysis and Recognition 9(2), 167–177 (2007)CrossRefGoogle Scholar
  32. 32.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: IEEE Computer Society Conf. in Computer Vision and Pattern Recognition, pp. 424–429 (2000)Google Scholar
  33. 33.
    Leydier, Y., Lebourgeois, F., Emptoz, H.: Text search for medieval manuscript images. Journal of Pattern Recognition 40(12), 3552–3567 (2007)zbMATHCrossRefGoogle Scholar
  34. 34.
    Li, L., Lu, S.J., Tan, C.L.: A fast keyword-spotting technique. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 68–72. IEEE Computer Society, Washington, DC, USA (2007)Google Scholar
  35. 35.
    Liang, S., Sun, Z.: Sketch retrieval and relevance feedback with biased SVM classification. Pattern Recognition Letters 29(12), 1733–1741 (2008)CrossRefGoogle Scholar
  36. 36.
    Licata, A., Psarrou, A., Kokla, V.: Revealing the visually unknown in ancient manuscripts with a similarity measure for IR-imaged inks. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 818–822. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  37. 37.
    Llados, J., Sanchez, G.: Indexing historical documents by word shape signatures. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 362–366. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  38. 38.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  39. 39.
    Lu, S., Li, L., Tan, C.L.: Document image retrieval through word shape coding. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(11), 1913–1918 (2008)CrossRefGoogle Scholar
  40. 40.
    Lu, S., Tan, C.: Keyword spotting and retrieval of document images captured by a digital camera. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 994–998. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  41. 41.
    Lu, S., Tan, C.L.: Retrieval of machine-printed latin documents through word shape coding. Journal of Pattern Recognition 41(5), 1816–1826 (2008)CrossRefGoogle Scholar
  42. 42.
    Lu, Y., Zhang, L., Tan, C.L.: Retrieving imaged documents in digital libraries based on word image coding. In: Proc. Int’l Workshop on Document Image Analysis for Libraries, pp. 174–187. IEEE Computer Society Press, Washington, DC, USA (2004)CrossRefGoogle Scholar
  43. 43.
    Manmatha, R., Han, C., Riseman, E.M.: Word spotting: A new approach to indexing handwriting. In: IEEE Computer Society Conf. in Computer Vision and Pattern Recognition, pp. 631–637. IEEE Computer Society, Los Alamitos (1996)Google Scholar
  44. 44.
    Marinai, S.: A Survey of Document Image Retrieval in Digital Libraries. In: Sulem, L.L. (ed.) Actes du 9ème Colloque International Francophone sur l’Ecrit et le Document, SDN 2006, pp. 193–198 (2006)Google Scholar
  45. 45.
    Marinai, S.: Text retrieval from early printed books. International Journal of Document Analysis and Recognition (2011); doi:10.1007/s10032-010-0146-0Google Scholar
  46. 46.
    Marinai, S., Faini, S., Marino, E., Soda, G.: Efficient word retrieval by means of SOM clustering and PCA. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 336–347. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  47. 47.
    Marinai, S., Gori, M., Soda, G.: Artificial neural networks for document analysis and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(1), 23–35 (2005)CrossRefGoogle Scholar
  48. 48.
    Marinai, S., Marino, E., Soda, G.: Layout based document image retrieval by means of XY tree reduction. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 432–436 (2005)Google Scholar
  49. 49.
    Marinai, S., Marino, E., Soda, G.: Font adaptive word indexing of modern printed documents. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(8) (2006)Google Scholar
  50. 50.
    Marinai, S., Marino, E., Soda, G.: Tree clustering for layout-based document image retrieval. In: Proc. Int’l Workshop on Document Image Analysis for Libraries, pp. 243–251 (2006)Google Scholar
  51. 51.
    Marinai, S., Miotti, B., Soda, G.: Mathematical symbol indexing using topologically ordered clusters of shape contexts. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 1041–1045 (2009)Google Scholar
  52. 52.
    Marinai, S., Miotti, B., Soda, G.: Bag of characters and SOM clustering for script recognition and writer identification. In: Proc. Int’l Conf. on Pattern Recognition, pp. 2182–2185 (2010)Google Scholar
  53. 53.
    Meshesha, M., Jawahar, C.V.: Matching word images for content-based retrieval from printed document images. International Journal of Document Analysis and Recognition 11(1), 29–38 (2008)CrossRefGoogle Scholar
  54. 54.
    Mitra, M., Chaudhuri, B.: Information retrieval from documents: A survey. Information Retrieval 2(2/3), 141–163 (2000)CrossRefGoogle Scholar
  55. 55.
    Moghaddam, R., Cheriet, M.: Application of multi-level classifiers and clustering for automatic word spotting in historical document images. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 511–515. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  56. 56.
    Nakai, T., Kise, K., Iwamura, M.: Real-time retrieval for images of documents in various languages using a web camera. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 146–150. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  57. 57.
    Nguyen, T.O., Tabbone, S., Terrades, O.R.: Symbol descriptor based on shape context and vector model of information retrieval. In: Proc. IAPR Int’l Workshop on Document Analysis Systems, pp. 191–197. IEEE Computer Society, Washington, DC, USA (2008)CrossRefGoogle Scholar
  58. 58.
    Perronnin, F.: Universal and adapted vocabularies for generic visual categorization. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(7), 1243–1256 (2008)CrossRefGoogle Scholar
  59. 59.
    Qureshi, R.J., Ramel, J.-Y., Barret, D., Cardot, H.: Spotting symbols in line drawing images using graph representations. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 91–103. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  60. 60.
    Rath, T.M., Manmatha, R.: Features for word spotting in historical manuscripts. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 218–222. IEEE Computer Society Press, Washington, DC, USA (2003)CrossRefGoogle Scholar
  61. 61.
    Rath, T.M., Manmatha, R.: Word spotting for historical documents. International Journal of Document Analysis and Recognition 9(2), 139–152 (2007)CrossRefGoogle Scholar
  62. 62.
    Rodriguez, J.A., Perronnin, F.: Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proc. Int’l Conf. on Handwriting Recognition (2008)Google Scholar
  63. 63.
    Rodriguez-Serrano, J., Perronnin, F.: Handwritten word-image retrieval with synthesized typed queries. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 351–355. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  64. 64.
    Rusiñol, M., Lladós, J.: Symbol spotting in technical drawings using vectorial signatures. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 35–46. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  65. 65.
    Rusiñol, M., Lladós, J.: A region-based hashing approach for symbol spotting in technical documents. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 104–113. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  66. 66.
    Rusiñol, M., Lladós, J.: Word and symbol spotting using spatial organization of local descriptors. In: Proc. IAPR Int’l Workshop on Document Analysis Systems, pp. 489–496. IEEE Computer Society Press, Washington, DC, USA (2008)CrossRefGoogle Scholar
  67. 67.
    Rusiñol, M., Lladós, J.: Symbol Spotting in Digital Libraries: Focused Retrieval over Graphic-rich Document Collections. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  68. 68.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)zbMATHCrossRefGoogle Scholar
  69. 69.
    Schomaker, L.: Retrieval of handwritten lines in historical documents. In: Proc. Int’l Conf. on Document Analysis and Recognition, vol. 2, pp. 594–598 (2007)Google Scholar
  70. 70.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proc. Int’l Conf. on Computer Vision, vol. 2, pp. 1470–1477. IEEE Computer Society Press, Los Alamitos (2003)CrossRefGoogle Scholar
  71. 71.
    Smeaton, A.F., Spitz, A.L.: Using character shape coding for information retrieval. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 974–978 (1997)Google Scholar
  72. 72.
    Super, B.J.: Retrieval from shape databases using chance probability functions and fixed correspondence. International Journal of Pattern Recognition and Artificial Intelligence 20(8), 1117–1138 (2006)CrossRefGoogle Scholar
  73. 73.
    Tahmasebi, N., Niklas, K., Theuerkauf, T., Risse, T.: Using word sense discrimination on historic document collections. In: Proc. Joint Conf. on Digital Libraries, pp. 89–98. ACM, New York (2010)Google Scholar
  74. 74.
    Tan, G., Viard-Gaudin, C., Kot, A.: Information retrieval model for online handwritten script identification. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 336–340. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar
  75. 75.
    Terasawa, K., Nagasaki, T., Kawashima, T.: Eigenspace method for text retrieval in historical documents. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 437–441 (2005)Google Scholar
  76. 76.
    Tzacheva, A., El-Sonbaty, Y., El-Kwae, E.A.: Document image matching using a maximal grid approach. In: Proc. SPIE Document Recognition and Retrieval IX, pp. 121–128 (2002)Google Scholar
  77. 77.
    Uttama, S., Loonis, P., Delalandre, M., Ogier, J.M.: Segmentation and retrieval of ancient graphic documents. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 88–98. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  78. 78.
    Wan, G., Liu, Z.: Content-based information retrieval and digital libraries. Information Technology & Libraries 27, 41–47 (2008)Google Scholar
  79. 79.
    Waters, D., Garrett, J.: Preserving digital information. report of the task force on archiving of digital information. Tech. rep., The Commission on Preservation and Access (1996)Google Scholar
  80. 80.
    Wei, C.H., Li, Y., Chau, W.Y., Li, C.T.: Trademark image retrieval using synthetic features for describing global shape and interior structure. Journal of Pattern Recognition 42(3), 386–394 (2009)zbMATHCrossRefGoogle Scholar
  81. 81.
    Witten, I.H., Bainbridge, D.: How to Build a Digital Library. Elsevier Science Inc., New York (2002)Google Scholar
  82. 82.
    Wong, W.T., Shih, F.Y., Su, T.F.: Shape-based image retrieval using two-level similarity measures. International Journal of Pattern Recognition and Artificial Intelligence 21(6), 995–1015 (2007)CrossRefGoogle Scholar
  83. 83.
    Zhang, B., Srihari, S., Huang, C.: Word image retrieval using binary features. In: SPIE, Document Recognition and Retrieval XI, pp. 45–53 (2004)Google Scholar
  84. 84.
    Zhang, W., Liu, W.: A new vectorial signature for quick symbol indexing, filtering and recognition. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 536–540. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  85. 85.
    Zhang, Z., Jin, L., Ding, K., Gao, X.: Character-SIFT: a novel feature for offline handwritten chinese character recognition. In: Proc. Int’l Conf. on Document Analysis and Recognition, pp. 763–767. IEEE Computer Society Press, Los Alamitos (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Simone Marinai
    • 1
  • Beatrice Miotti
    • 1
  • Giovanni Soda
    • 1
  1. 1.University of FlorenceItaly

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