Estimating the AGE of Birch Bark Manuscripts using Computational Paleography

We propose a novel method for automatically estimating the age of birch bark manuscripts based solely on the appearance of graphemes (paleographic dating). Our method achieves mean absolute accuracy of 18.9 years which is comparable to or surpasses the performance of human experts and of other computational paleography studies.

Keywords

Received

01.10.2018

Publication date

07.10.2018

Number of characters

720

Cite Download pdf To download PDF you should sign in

GOST	Sidorov K. Estimating the AGE of Birch Bark Manuscripts using Computational Paleography // Programmirovanie – 2018. – Issue 4 C. 86-96 [Electronic resource]. URL: http://ras.jes.su/progr/s207987840000142-5-1 (circulation date: 05.05.2024). DOI: 10.31857/S013234740000698-2
MLA	Sidorov, Kirill "Estimating the AGE of Birch Bark Manuscripts using Computational Paleography." Programmirovanie 4 (2018):86-96. DOI: 10.31857/S013234740000698-2
APA	Sidorov K. (2018). Estimating the AGE of Birch Bark Manuscripts using Computational Paleography. Programmirovanie (4), pp.86-96 DOI: 10.31857/S013234740000698-2

Размещенный ниже текст является ознакомительной версией и может не соответствовать печатной

Readers community rating: votes 0

1. Yanin V.L. and Zaliznyal A.A. Novorodskiye gramoty na bereste (iz raskopok 1990–1996 godov), Moscow: Nauka, 2000.

2. He S., Samara P., Burgers J., Schomaker L. Towards style-based dating of historical documents, 14th International Conference on Frontiers in Handwriting Recognition, 2014, pp. 265–270.

3. He S., Schomaker, L. A polar stroke descriptor for classiﬁcation of historical documents, 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, pp. 6–10.

4. He S., Samara P., Burgers J., Schomaker L. Discovering visual element evolutions for historical document dating, 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016, pp. 7–12.

5. He S., Samara P., Burgers J., Schomaker L. Historical document dating using unsupervised attribute learning, 12th IAPR Workshop on DocumentAnalysisSystems(DAS),IEEEComputer Soc., 2016, pp. 36–41.

6. He S., Samara P., Burgers J., Schomaker L. A multiple-label guided clustering algorithm for historical document dating and localization, IEEE Trans. Image Process., 2016, vol. 25, no. 11, pp. 5252–5265.

7. Wahlberg F., M˚artensson L., Brun A. Large scale style based dating of medieval manuscripts, Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing, HIP’15, 2015, pp. 107–114.

8. Birchbark literacy from Medieval Rus: Contents and contexts (digital archive). http://gramoty.ru.

9. Cootes T.F., Edwards G.J., Taylor C.J. Active appearance models, IEEE Trans. Pattern Anal. Mach. Intell., 2001, vol. 23, no. 6, pp. 681–685.

10. Cootes T., Taylor C.Statisticalmodelsofappearance for computer vision, Technical report, University of Manchester, 2004.

11. Alexa M., Mu¨ller W. Representing animations by principal components, Comput. Graph. Forum, 2000, vol. 19, no. 3, pp. 411–418.

12. Kry P.G., James D.L., Pai D.K. EigenSkin: Real time large deformation character skinning in hardware, Proceedings of the 2002 ACM SIGGRAPH/EurographicsSymposiumonComputer Animation, ACM, 2002, pp. 153–159.

13. Cootes T.F., Twining C.J., Petrovic V.S., Babalola K.O., Taylor C.J. Computing accurate correspondences across groups of images, IEEE Trans. Pattern Anal. Mach. Intell., 2010, vol. 32, no. 11, pp. 1994–2005.

14. Sidorov K., Richmond R., Marshall D. An eﬃcient stochastic approach to groupwise non-rigid image registration, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Soc., 2009, pp. 2208–2213.

15. Cootes T.F., Twining C.J., Petrovic V., Schestowitz R., Taylor C.J.Groupwiseconstructionofappearance models using piece-wise aﬃne deformations, Proceedings of British Machine Vision Conference, 2005, pp. 879–888.

16. Song X., Myronenko A. Point set registration: Coherent point drift, IEEE Trans. Pattern Anal. Machine Intelligence, 2010, vol. 32, pp. 2262–2275.

17. Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Shuai B., Liu T., Wang X., Wang G. Recent advances in convolutional neural networks, CoRR, vol. abs/1512.07108.

18. LeCun Y., Bengio Y., Hinton G. Deep learning, Nature, 2015, vol. 521, no. 7553, pp. 436–444.

19. Tommasi T., Orabona F., Caputo B. Safety in numbers: Learning categories from few examples with multi model knowledge transfer, Proceedings of IEEE Computer Vision and Pattern Recognition Conference, IEEE, 2010, pp. 3081–3088.

20. Aytar Y., Zisserman A. Tabula rasa: Model transfer for object category detection, International ConferenceonComputerVision,2011,pp. 2252–2259.

21. Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R. Dropout: A simple way to prevent neural networks from overﬁtting, J. Mach. Learn. Res., 2014, vol. 15, pp. 1929–1958.

22. Vedaldi A., Lenc K. MatConvNet: Convolutional neural networks for MATLAB, in Proc. the ACM International Conference on Multimedia, 2015.

23. Kuncheva L.I. Combining Pattern Classiﬁers: Methods and Algorithms, Wiley, 2004.

24. Li Y., Genzel D., Fujii Y., Popat A.C. Publication date estimation for printed historical documents using convolutional neural networks, Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing, HIP’15, New York, NY, USA: ACM, 2015, pp. 99–106.

25. van der Maaten L., Hinton G.E. Visualizing highdimensional data using t-SNE, J. Mach. Learn. Res., 2008, vol. 9, pp. 2579–2605. 26. Zeiler M.D., Fergus R.Visualizingand Understanding Convolutional Networks,Springer,2014,pp. 818–833.

26. Zeiler M.D., Fergus R. Visualizing and Understanding Convolutional Networks, Springer, 2014, pp. 818–833.