Skip to main content

READ for Solving Manuscript Riddles: A Preliminary Study of the Manuscripts of the 3rd ṣaṭka of the Jayadrathayāmala

  • Conference paper
  • First Online:
Document Analysis and Recognition – ICDAR 2021 Workshops (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12917))

Included in the following conference series:

Abstract

This is a part of an in-depth study of a set of the manuscripts related to the Jayadrathayāmala. Taking JY.3.9 as a test-chapter, a comparative paleography analysis of the 11 manuscripts was made within READ software framework. The workflow within READ minimized the effort to make a few important discoveries (manuscripts containing more than one script, identification of the manuscripts potentially written by the same person) as well as to create an overview of the shift from Nāgarī to Newārī and, finally, to Devanāgarī scripts within the history of manuscript transmission of a single chapter. Exploratory statistical analysis in R of the syllable frequency in each manuscript, based on the paleography analysis export from READ, helped to establish that there are potentially two lines of manuscript transmission of the JY.3.9.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Pers. comm. 05.04.2021.

  2. 2.

    READsoftware 2021: https://github.com/readsoftware/read/wiki.

  3. 3.

    Based on Gupta [n.d.]. See also Fig. 5 here.

  4. 4.

    For clustering function dfm_weight, library quanteda in R. Visualisation - pheatmap. We thank Dmitry Serbaev, Omsk, Russia, for his expert advise concerning R.

  5. 5.

    For the historical changes in Sanskrit orthography in a close comparison to Middle-Indic languages see Edgerton 1946:199, 202–203.

References

  1. Benoit, K., et al. Quanteda: an R package for the quantitative analysis of textual data. J. Open Sour. Softw. 3(30), 774 (2018). https://doi.org/10.21105/joss.00774, https://quanteda.io

  2. Edgerton, F.: Meter, phonology, and orthography in Buddhist Hybrid Sanskrit. J. Am. Orient. Soc. 66(3), 197–206 (1946). https://www.jstor.org/stable/595566

  3. Gupta, P. L.: Medieval Indian Alphabets: [Nagari, North India]. All Indian Educational Supply Company, Delhi (n.d., pre 2012)

    Google Scholar 

  4. [Jayadrathayāmala, ṣaṭka 3]: A NGMPP Microfilm A152/9; B NGMPP Microfilm B26/9; C NGMPP Microfilm A1312/25; D NGMPP Microfilm B122/3; E NGMPP Microfilm C72/1, G NGMPP Microfilm B121/13, I NGMPP Microfilm C47/03

    Google Scholar 

  5. [Jayadrathayāmala, ṣaṭka 3]: K. https://digital.staatsbibliothek-berlin.de/werkansicht?PPN=PPN898781620&PHYSID=PHYS_0004. Accessed 23 May 2021

  6. [Jayadrathayāmalamantroddhāraṭippa ī]: A NGMPP Microfilm B122/07; B NGMPP Microfilm A1267/3; O NGMPP Microfilm A152/8

    Google Scholar 

  7. NGMPP (Nepal-German Manuscript Preservation Project) Catalogue, https://catalogue.ngmcp.uni-hamburg.de/content/search/ngmcpdocument.xed. Accessed 22 May 2021

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Olga Serbaeva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Serbaeva, O., White, S. (2021). READ for Solving Manuscript Riddles: A Preliminary Study of the Manuscripts of the 3rd ṣaṭka of the Jayadrathayāmala. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86159-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86158-2

  • Online ISBN: 978-3-030-86159-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics