scholarly journals Words and Pictures: Rāmāyaṇa Traditions and the Art of Ekphrasis

Religions ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 364
Author(s):  
Subhashini Kaligotla
Keyword(s):  
South Asian ◽  
Creative Process ◽  
Formal Analysis ◽  
17Th Century ◽  
Asian Studies ◽  
Standard View ◽  
South Asian Studies ◽  
Epic Poem

This article examines two ambitious enactments of the Rama story or Rāmāyaṇa, side by side: the 17th-century painted Mewar Rāmāyaṇa and Vālmīki’s epic poem (ca. 750–500 BCE). Through a formal analysis of two crucial episodes of the tale, it highlights the creative tactics of each medium and stresses their separate aesthetic interests and autonomy. While A. K. Ramanujan’s notion of the “telling” has been immensely influential in South Asian studies to theorize the Rāmāyaṇa’s multiplicity, that concept tends to privilege speech-based embodiments. I propose, by contrast, that ekphrasis may be a more broadly applicable lens. Understanding ekphrasis as an enactment of the Rama story in any medium or in interart media, I advocate for considering poetry and painting on an equal plane as opposed to the standard view of the Mewar paintings as visual translations of linguistic phenomena. Ekphrasis, as a gateway to the maker’s creative process and preoccupations, is central to the paper’s argument, as is the role receivers play in the act of Rāmāyaṇa making.

scholarly journals Learning-free pattern detection for manuscript research:

2021 ◽  
Author(s):  
Hussein Mohammed ◽  
Volker Märgner ◽  
Giovanni Ciotti
Keyword(s):  
South Asian ◽  
State Of The Art ◽  
Research Question ◽  
Software Tool ◽  
Pattern Detection ◽  
Asian Studies ◽  
Medieval Manuscripts ◽  
South Asian Studies ◽  
Training Samples ◽  
Nearest Neighbour Classifier

AbstractAutomatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.

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