Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia
and are comparable in amount and relevance to texts written in Latin or ancient Greek …

Handwritten text recognition in low resource scenarios, such as manuscripts with rare
alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based …

The cuneiform script provides a glimpse into our ancient history. However, reading age-old
clay tablets is time-consuming and requires years of training. To simplify this process, we …

In this paper we present a new method for automatic transliteration and segmentation of
Unicode cuneiform glyphs using Natural Language Processing (NLP) techniques …

This thesis explores the use of Natural Language Processing (NLP) on the ancient
Mesopotamian primary sources, especially those written in the Akkadian language …

Automated content-based search for arbitrary cuneiform signs in photographic reproductions
is a challenging task in the analysis of ancient documents, a central component of which is a …

The efficient use of machine learning methods in the context of word spotting on cuneiform
datasets is usually limited by the number of annotated training samples available. In view of …

Intermediate representations based on binary attributes are widely used for image retrieval
tasks. Especially for the task of word spotting, attribute representations continually achieves …

Recent breakthroughs in Artificial Intelligence, Deep Learning, and Document Image
Analysis and Recognition have significantly eased the creation of digital libraries and the …

Text line segmentation is a key step in Tibetan historical document recognition. A novel
method for text line segmentation was proposed based on the baseline in uchen Tibetan …