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Assembling Fragments of Ancient Papyrus via Artificial Intelligence

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Pervasive Knowledge and Collective Intelligence on Web and Social Media (PerSOM 2022)

Abstract

The knowledge of humanity passes through the ancient texts whose acquisition, reconstruction and interpretation become tasks of fundamental importance. The simultaneous spread of equipment capable of exploiting new technologies for data acquisition together with the opportunities offered by Artificial Intelligence open new unimaginable horizons in different applications including the conservation of cultural heritage. In this work, we refer to the opportunity inherent the acquisition of texts from papyri via machine learning and deep learning applications. The theme of assembling fragments, will be investigated by referring to some recent interesting contributions of the scientific community.

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References

  1. Iv. the herculaneum papyri. Bull. Inst. Class. Stud. 33(S54), 36–45 (1986). https://doi.org/10.1111/j.2041-5370.1986.tb01374.x

  2. Abitbol, R., Shimshoni, I., Ben-Dov, J.: Machine learning based assembly of fragments of ancient papyrus. J. Comput. Cult. Heritage (JOCCH) 14(3), 1–21 (2021)

    Article  Google Scholar 

  3. Berlino, A., Caroprese, L., La Marca, A., Vocaturo, E., Zumpano, E.: Augmented reality for the enhancement of archaeological heritage: a Calabrian experience. In: CEUR Workshop Proceedings, vol. 2320, pp. 86–94 (2019)

    Google Scholar 

  4. Berlino, A., Caroprese, L., Vocaturo, E., Zumpano, E.: A mobile application for the enhancement of POIs in Calabria. In: VIPERC@ IRCDL, pp. 13–25 (2020)

    Google Scholar 

  5. Bukreeva, I., et al.: Virtual unrolling and deciphering of Herculaneum papyri by x-ray phase-contrast tomography. Sci. Rep. 6(1), 1–7 (2016)

    Google Scholar 

  6. Bülow-Jacobsen, A.: Writing materials in the ancient world (2011)

    Google Scholar 

  7. Calautti, M., Caroprese, L., Greco, S., Molinaro, C., Trubitsyna, I., Zumpano, E.: Existential active integrity constraints. Expert Syst. Appl. 168, 114297 (2021). https://doi.org/10.1016/j.eswa.2020.114297

    Article  Google Scholar 

  8. Caroprese, L., Trubitsyna, I., Truszczynski, M., Zumpano, E.: A measure of arbitrariness in abductive explanations. Theory Pract. Log. Program. 14(4–5), 665–679 (2014). https://doi.org/10.1017/S1471068414000271

    Article  MathSciNet  MATH  Google Scholar 

  9. Caroprese, L., Vocaturo, E., Zumpano, E.: Features for melanoma lesions: extraction and classification. In: WI 2019, pp. 238–243. ACM (2019). https://doi.org/10.1145/3358695.3360898

  10. Caroprese, L., Vocaturo, E., Zumpano, E.: Argumentation approaches for explanaible AI in medical informatics. Intell. Syst. Appl. 16, 200109 (2022). https://doi.org/10.1016/j.iswa.2022.200109

    Article  Google Scholar 

  11. Caroprese, L., Zumpano, E.: Aggregates and priorities in P2P data management systems. In: Desai, B.C., Cruz, I.F., Bernardino, J. (eds.) 15th International Database Engineering and Applications Symposium (IDEAS 2011), 21–27 September 2011, Lisbon, Portugal, pp. 1–7. ACM (2011). https://doi.org/10.1145/2076623.2076625

  12. Caroprese, L., Zumpano, E.: Handling preferences in P2P systems. In: Lukasiewicz, T., Sali, A. (eds.) FoIKS 2012. LNCS, vol. 7153, pp. 91–106. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28472-4_6

    Chapter  Google Scholar 

  13. Caroprese, L., Zumpano, E.: Restoring consistency in P2P deductive databases. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS (LNAI), vol. 7520, pp. 168–179. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33362-0_13

    Chapter  Google Scholar 

  14. Caroprese, L., Zumpano, E.: A logic framework for P2P deductive databases. Theory Pract. Log. Program. 20(1), 1–43 (2020). https://doi.org/10.1017/S1471068419000073

    Article  MathSciNet  MATH  Google Scholar 

  15. Caroprese, L., Zumpano, E.: Semantic data management in P2P systems driven by self-esteem. J. Log. Comput. 32(5), 871–901 (2022). https://doi.org/10.1093/logcom/exac001

    Article  MathSciNet  MATH  Google Scholar 

  16. Christlein, V., Gropp, M., Fiel, S., Maier, A.: Unsupervised feature learning for writer identification and writer retrieval. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 991–997. IEEE (2017)

    Google Scholar 

  17. Chung, M.G., Fleck, M.M., Forsyth, D.A.: Jigsaw puzzle solver using shape and color. In: ICSP 1998. 1998 Fourth International Conference on Signal Processing (Cat. No. 98TH8344), vol. 2, pp. 877–880. IEEE (1998)

    Google Scholar 

  18. Frösén, J.: Conservation of ancient papyrus materials (2011)

    Google Scholar 

  19. Fuduli, A., Veltri, P., Vocaturo, E., Zumpano, E.: Melanoma detection using color and texture features in computer vision systems. Adv. Sci. Technol. Eng. Syst. J. 4(5), 16–22 (2019)

    Article  Google Scholar 

  20. Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7

    Chapter  Google Scholar 

  21. Kaya, M., Bilge, H.Ş: Deep metric learning: a survey. Symmetry 11(9), 1066 (2019)

    Google Scholar 

  22. Koch, G., Zemel, R., Salakhutdinov, R., et al.: Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol. 2, p. 0. Lille (2015)

    Google Scholar 

  23. Kong, W., Kimia, B.B.: On solving 2D and 3D puzzles using curve matching. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, pp. II-II. IEEE (2001)

    Google Scholar 

  24. Kosiba, D.A., Devaux, P.M., Balasubramanian, S., Gandhi, T.L., Kasturi, K.: An automatic jigsaw puzzle solver. In: Proceedings of 12th International conference on pattern recognition, vol. 1, pp. 616–618. IEEE (1994)

    Google Scholar 

  25. Labaune, J., Jackson, J., Duling, I., Menu, M., Mourou, G., et al.: Papyrus imaging with terahertz time domain spectroscopy. Appl. Phys. A 100(3), 607–612 (2010)

    Article  Google Scholar 

  26. Leach, B.: Papyrus manufacture. UCLA Encycl. Egyptology 1(1) (2009)

    Google Scholar 

  27. Lombardi, F., Marinai, S.: Deep learning for historical document analysis and recognition-a survey. J. Imaging 6(10), 110 (2020)

    Article  Google Scholar 

  28. Misra, I., Maaten, L.V.D.: Self-supervised learning of pretext-invariant representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6707–6717 (2020)

    Google Scholar 

  29. Paixão, T.M., et al.: Self-supervised deep reconstruction of mixed strip-shredded text documents. Pattern Recogn. 107, 107535 (2020)

    Article  Google Scholar 

  30. Pirrone, A., Aimar, M.B., Journet, N.: Papy-S-Net: a Siamese network to match papyrus fragments. In: Proceedings of the 5th International Workshop on Historical Document Imaging and Processing, pp. 78–83 (2019)

    Google Scholar 

  31. Pirrone, A., Beurton-Aimar, M., Journet, N.: Self-supervised deep metric learning for ancient papyrus fragments retrieval. Int. J. Doc. Anal. Recogn. (IJDAR) 24(3), 219–234 (2021). https://doi.org/10.1007/s10032-021-00369-1

    Article  Google Scholar 

  32. Romain, K., Abdel, B.: Semi-supervised learning through adversary networks for baseline detection. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 128–133. IEEE (2019)

    Google Scholar 

  33. Sizikova, E., Funkhouser, T.: Wall painting reconstruction using a genetic algorithm. J. Comput. Cult. Heritage (JOCCH) 11(1), 1–17 (2017)

    Google Scholar 

  34. Studer, L., et al.: A comprehensive study of ImageNet pre-training for historical document image analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 720–725. IEEE (2019)

    Google Scholar 

  35. Tang, Y., Peng, L., Xu, Q., Wang, Y., Furuhata, A.: CNN based transfer learning for historical Chinese character recognition. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 25–29. IEEE (2016)

    Google Scholar 

  36. Thompson, E.M.: An Introduction to Greek and Latin Palaeography. Cambridge University Press, Cambridge (2013)

    Book  Google Scholar 

  37. Toler-Franklin, C., Brown, B., Weyrich, T., Funkhouser, T., Rusinkiewicz, S.: Multi-feature matching of fresco fragments. ACM Trans. Graph. (TOG) 29(6), 1–12 (2010)

    Article  Google Scholar 

  38. Tsamoura, E., Pitas, I.: Automatic color based reassembly of fragmented images and paintings. IEEE Trans. Image Process. 19(3), 680–690 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  39. Vocaturo, E., Zumpano, E., Caroprese, L., Pagliuso, S.M., Lappano, D.: Educational games for cultural heritage. In: VIPERC@ IRCDL, pp. 95–106 (2019)

    Google Scholar 

  40. Vocaturo, E., Zumpano, E., Giallombardo, G., Miglionico, G.: DC-SMIL: a multiple instance learning solution via spherical separation for automated detection of displastyc nevi. In: IDEAS 2020, pp. 4:1–4:9. ACM (2020). https://doi.org/10.1145/3410566.3410611

  41. Zumpano, E., Fuduli, A., Vocaturo, E., Avolio, M.: Viral pneumonia images classification by multiple instance learning: preliminary results. In: IDEAS 2021, pp. 292–296. ACM (2021)

    Google Scholar 

  42. Zumpano, E., et al.: Simpatico 3D mobile for diagnostic procedures. In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, pp. 468–472 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. DIMES, University of Calabria, Arcavacata, Italy

    Eugenio Vocaturo & Ester Zumpano

  2. CNR-Nanotec, Rende, CS, Italy

    Eugenio Vocaturo & Ester Zumpano

Authors
  1. Eugenio Vocaturo
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  2. Ester Zumpano
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Corresponding author

Correspondence to Eugenio Vocaturo .

Editor information

Editors and Affiliations

  1. ICAR-CNR, University of Calabria, Rende, Italy

    Carmela Comito

  2. University of Calabria, Rende, Italy

    Domenico Talia

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Vocaturo, E., Zumpano, E. (2023). Assembling Fragments of Ancient Papyrus via Artificial Intelligence. In: Comito, C., Talia, D. (eds) Pervasive Knowledge and Collective Intelligence on Web and Social Media. PerSOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-31469-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-31469-8_1

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