Skip to main content

XDOCS: An Application to Index Historical Documents

  • Conference paper
  • First Online:
Digital Libraries and Multimedia Archives (IRCDL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 806))

Included in the following conference series:

Abstract

Dematerialization and digitalization of historical documents are key elements for their availability, preservation and diffusion. Unfortunately, the conversion from handwritten to digitalized documents presents several technical challenges.

The XDOCS project is created with the main goal of making available and extending the usability of historical documents for a great variety of audience, like scholars, institutions and libraries. In this paper, the core elements of XDOCS, i.e. page dewarping and word spotting technique, are described and two new applications, i.e. annotation/indexing and search tool, are presented.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    http://imagelab.ing.unimore.it/XDOCS.

References

  1. Balducci, F., Borghi, G.: An annotation tool for a digital library system of epidermal data. In: Grana, C., Baraldi, L. (eds.) IRCDL 2017. CCIS, vol. 733, pp. 173–186. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68130-6_14

    Chapter  Google Scholar 

  2. Bolelli, F.: Indexing of historical document images: ad hoc dewarping technique for handwritten text. In: Grana, C., Baraldi, L. (eds.) IRCDL 2017. CCIS, vol. 733, pp. 45–55. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68130-6_4

    Chapter  Google Scholar 

  3. Bolelli, F., Borghi, G., Grana, C.: Historical handwritten text images word spotting through sliding window HOG features. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 729–738. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68560-1_65

    Chapter  Google Scholar 

  4. Cao, H., Ding, X., Liu, C.: Rectifying the bound document image captured by the camera: a model based approach. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, pp. 71–74. IEEE (2003)

    Google Scholar 

  5. Corbelli, A., Baraldi, L., Balducci, F., Grana, C., Cucchiara, R.: Layout analysis and content classification in digitized books. In: Agosti, M., Bertini, M., Ferilli, S., Marinai, S., Orio, N. (eds.) IRCDL 2016. CCIS, vol. 701, pp. 153–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56300-8_14

    Chapter  Google Scholar 

  6. Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)

    Article  MATH  Google Scholar 

  7. Fu, B., Wu, M., Li, R., Li, W., Xu, Z., Yang, C.: A model-based book dewarping method using text line detection. In: Proceedings of the 2nd International Workshop on Camera Based Document Analysis and Recognition, Curitiba, Barazil, pp. 63–70 (2007)

    Google Scholar 

  8. Gatos, B., Pratikakis, I., Ntirogiannis, K.: Segmentation based recovery of arbitrarily warped document images. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 989–993. IEEE (2007)

    Google Scholar 

  9. Kolcz, A., Alspector, J., Augusteijn, M., Carlson, R., Popescu, G.V.: A line-oriented approach to word spotting in handwritten documents. Pattern Anal. Appl. 3(2), 153–168 (2000)

    Article  Google Scholar 

  10. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  11. Manmatha, R., Croft, W.: Word spotting: Indexing handwritten archives. In: Intelligent Multimedia Information Retrieval Collection, pp. 43–64 (1997)

    Google Scholar 

  12. Manmatha, R., Han, C., Riseman, E.M., Croft, W.B.: Indexing handwriting using word matching. In: Proceedings of the first ACM International Conference on Digital Libraries, pp. 151–159. ACM (1996)

    Google Scholar 

  13. Pini, S., Cornia, M., Baraldi, L., Cucchiara, R.: Towards video captioning with naming: a novel dataset and a multi-modal approach. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10485, pp. 384–395. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68548-9_36

    Chapter  Google Scholar 

  14. Rath, T.M., Manmatha, R.: Features for word spotting in historical manuscripts. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, pp. 218–222. IEEE (2003)

    Google Scholar 

  15. Rodriguez, J.A., Perronnin, F.: Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proceedings of the 1st ICFHR, pp. 7–12 (2008)

    Google Scholar 

  16. Stamatopoulos, N., Gatos, B., Pratikakis, I., Perantonis, S.J.: A two-step dewarping of camera document images. In: The Eighth IAPR International Workshop on Document Analysis Systems, DAS 2008, pp. 209–216. IEEE (2008)

    Google Scholar 

  17. Terasawa, K., Nagasaki, T., Kawashima, T.: Eigenspace method for text retrieval in historical document images. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 437–441. IEEE (2005)

    Google Scholar 

  18. Terasawa, K., Tanaka, Y.: Slit style hog feature for document image word spotting. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 116–120. IEEE (2009)

    Google Scholar 

  19. Tomai, C.I., Zhang, B., Govindaraju, V.: Transcript mapping for historic handwritten document images. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 413–418. IEEE (2002)

    Google Scholar 

  20. Ulges, A., Lampert, C.H., Breuel, T.M.: Document image dewarping using robust estimation of curled text lines. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 1001–1005. IEEE (2005)

    Google Scholar 

Download references

Acknowledgement

The XDOCS project is currently underway at SATA s.r.l. in collaboration with the University of Modena and Reggio-Emilia, and co-funded by the Emilia-Romagna regional administration.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Federico Bolelli .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bolelli, F., Borghi, G., Grana, C. (2018). XDOCS: An Application to Index Historical Documents. In: Serra, G., Tasso, C. (eds) Digital Libraries and Multimedia Archives. IRCDL 2018. Communications in Computer and Information Science, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-319-73165-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73165-0_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73164-3

  • Online ISBN: 978-3-319-73165-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics