Abstract
Palaeography aims to study ancient documents and the identification of the people who participated in the handwriting process of a given document is one of the most important problems. To this aim, expert paleographers typically analyze handwriting features such as letter heights and widths, distances between characters and angles of inclination. With the aim of achieving more precise measures and also thanks to the availability of high-quality digital images, paleographers are starting to use digital tools. In this context, in previous studies, we proposed a pattern recognition system for distinguishing the writers of mediaeval books and also investigated which is the minimum amount of training data needed to achieve satisfactory results in terms of accuracy. In this paper, we present a reject option that allows us to implement a highly-reliable system for writer identification, trained on a reduced set of data. The experimental results, performed on two sets of digital images from medieval Bibles, show that rejecting only a few samples it is possible to strongly reduce the error rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antonacopoulos, A., Downton, A.C.: Special issue on the analysis of historical documents. IJDAR 9(2–4), 75–77 (2007)
Bozzolo, C., Coq, D., Muzerelle, D., Ornato, E.: Noir et blanc. Premiers résultats d’une enquête sur la mise en page dans le livre médiéval. In: Il libro e il testo, Urbino, pp. 195–221 (1982)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)
Cilia, N., De Stefano, C., Fontanella, F., Scotto di Freca, A.: A ranking-based feature selection approach for handwritten character recognition. Pattern Recogn. Lett. 121, 77–86 (2018)
Ciula, A.: The palaeographical method under the light of a digital approach. In: Rehbein, M., Sahle, P., Schaßan, T. (eds.) Kodikologie und Paläographie im digitalen Zeitalter-Codicology and Palaeography in the Digital Age, pp. 219–237. Bod, Norderstedt (2009)
Cordella, L.P., De Stefano, C., Fontanella, F., Scotto di Freca, A.: A weighted majority vote strategy using bayesian networks. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 219–228. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_23
Cordella, L.P., De Stefano, C., Fontanella, F., Marrocco, C., Scotto di Freca, A.: Combining single class features for improving performance of a two stage classifier. In: 20th International Conference on Pattern Recognition (ICPR 2010), pp. 4352–4355. IEEE Computer Society (2010)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (2006)
De Stefano, C., D’Elia, C., Marcelli, A.: A dynamic approach to learning vector quantization. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 4, pp. 601–604 (August 2004)
De Stefano, C., D’Elia, C., Marcelli, A., Scotto di Freca, A.: Improving dynamic learning vector quantization. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 804–807 (August 2006)
De Stefano, C., Folino, G., Fontanella, F., Scotto Freca, A.: Using bayesian networks for selecting classifiers in GP ensembles. Inf. Sci. 258, 200–216 (2014)
De Stefano, C., Fontanella, F., Folino, G., di Freca, A.S.: A bayesian approach for combining ensembles of GP classifiers. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 26–35. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21557-5_5
De Stefano, C., Fontanella, F., Marrocco, C.: A GA-based feature selection algorithm for remote sensing images. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 285–294. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78761-7_29
De Stefano, C., Maniaci, M., Fontanella, F., Scotto Freca, A.: Layout measures for writer identification in mediaeval documents. Measurement 127, 443–452 (2018)
De Stefano, C., Maniaci, M., Fontanella, F., Scotto di Freca, A.: Reliable writer identification in medieval manuscripts through page layout features: The Avila Bible case. Eng. Appl. Artif. Intell. 72, 99–110 (2018)
De Stefano, C., D’Elia, C., Scotto di Freca, A., Marcelli, A.: Classifier combination by bayesian networks for handwriting recognition. Int. J. Pattern Recogn. Artif. Intell. 23(05), 887–905 (2009)
De Stefano, C., Fontanella, F., Maniaci, M., Scotto di Freca, A.: A method for scribe distinction in medieval manuscripts using page layout features. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6978, pp. 393–402. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24085-0_41
Dhali, M.A., He, S., Popovic, M., Tigchelaar, E., Schomaker, L.: A digital palaeographic approach towards writer identification in the dead sea scrolls. In: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM, pp. 693–702 (2017)
Dinstein, I., Shapira, Y.: Ancient hebraic handwriting identification with run-length histograms. IEEE Trans. Syst. Man Cybern. 12(3), 405–409 (1982)
Gurrado, M.: “Graphoskop”, uno strumento informatico per l’analisi paleografica quantitativa. In: Rehbein, M., Sahle, P., Schaßan, T. (eds.) Kodikologie und Paläographie im digitalen Zeitalter-Codicology and Palaeography in the Digital Age, pp. 251–259. Bod, Norderstedt (2009)
He, S., Samara, P., Burgers, J., Schomaker, L.: Image-based historical manuscript dating using contour and stroke fragments. Pattern Recogn. 58, 159–171 (2016)
Liang, Y., Fairhurst, M.C., Guest, R.M., Erbilek, M.: Automatic handwriting feature extraction, analysis and visualization in the context of digital palaeography. IJPRAI 30(4), 1653001 (2016). 1–26
Maniaci, M., Ornato, G.: Prime considerazioni sulla genesi e la storia della bibbia di avila. In: Miscellanea F. Magistrale (2010)
Quinlan, J.R.: C4. 5 Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Francisco (1993)
Schomaker, L., Franke, K., Bulacu, M.: Using codebooks of fragmented connected-component contours in forensic and historic writer identification. Pattern Recogn. Lett. 28(6), 719–727 (2007). Pattern Recognition in Cultural Heritage and Medical Applications
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto di Freca, A. (2019). Minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
