Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks

Observed methods of cuneiform tablet reconstruction in virtual and real world environments

The reconstruction of fragmented artefacts is a tedious process that consumes many valuable work hours of scholars' time. We believe that such work can be made more efficient via new techniques in interactive virtual environments. The purpose of this research is to explore approaches to the reconstruction of cuneiform tablets in the real and virtual environment, and to address the potential barriers to virtual reconstruction of fragments. In this paper we present the results of an experiment exploring the reconstruction strategies employed by individual users working with tablet fragments in real and virtual environments. Our findings have identified physical factors that users find important to the reconstruction process and further explored the subjective usefulness of stereoscopic 3D in the reconstruction process. Our results, presented as dynamic graphs of interaction, compare the precise order of movement and rotation interactions, and the frequency of interaction achieved by successful and unsuccessful participants with some surprising insights. We present evidence that certain interaction styles and behaviours characterise success in the reconstruction process

A collaborative artefact reconstruction environment

A novel collaborative artefact reconstruction environment design is presented that is informed by experimental task observation and participatory design. The motivation for the design was to enable collaborative human and computer effort in the reconstruction of fragmented cuneiform tablets: millennia-old clay tablets used for written communication in early human civilisation. Thousands of joining cuneiform tablet fragments are distributed within and between worldwide collections. The reconstruction of the tablets poses a complex 3D jigsaw puzzle with no physically tractable solution. In reconstruction experiments, participants collaborated synchronously and asynchronously on virtual and physical reconstruction tasks. Results are presented that demonstrate the difficulties experienced by human reconstructors in virtual tasks compared to physical tasks. Unlike computer counterparts, humans have difficulty identifying joins in virtual environments but, unlike computers, humans are averse to making incorrect joins. A successful reconstruction environment would marry the opposing strengths and weaknesses of humans and computers, and provide tools to support the communications and interactions of successful physical performance, in the virtual setting. The paper presents a taxonomy of the communications and interactions observed in successful physical and synchronous collaborative reconstruction tasks. Tools for the support of these communications and interactions were successfully incorporated in the “i3D” virtual environment design presented

Computational aspects of model acquisition and join geometry for the virtual reconstruction of the atrahasis cuneiform tablet

The epic of Atrahasis is one of the most famous pieces of ancient Mesopotamian literature. The account has survived millennia on sets of clay tablets inscribed with cuneiform script; a sophisticated early writing system comprising signs formed from wedge-shaped impressions. The third tablet belonging to one of the most complete copies of the Atrahasis epic is broken. For over fifty years, one fragment, held in Geneva, was believed to join with another held in London. However, due to their 1000 km separation, the join had never been physically tested. This paper contributes a technological account of the successful virtual joining of the fragments [1]; the first ever longdistance virtual join of its type

Searching the past in the future: joining cuneiform tablet fragments in virtual collections

Joining cuneiform tablet fragments are separated within and between collections worldwide. In previous work of the Virtual Cuneiform Tablet Reconstruction Project [VCTR, 2018], automated joins were achieved for virtual 3D Ur and Uruk fragments held within the same collections. By virtue of this fact, these physical fragments were in close proximity to each other and, therefore, manual verification of each join could be readily achieved. Now, for the first time, a long-distance join is reported between cuneiform tablet fragments separated by 1000 km

Searching the past in the future: joining cuneiform tablet fragments in virtual collections

Joining cuneiform tablet fragments are separated within and between collections worldwide. In previous work of the Virtual Cuneiform Tablet Reconstruction Project [VCTR, 2018], automated joins were achieved for virtual 3D Ur and Uruk fragments held within the same collections. By virtue of this fact, these physical fragments were in close proximity to each other and, therefore, manual verification of each join could be readily achieved. Now, for the first time, a long-distance join is reported between cuneiform tablet fragments separated by 1000 km

The reconstruction of virtual cuneiform fragments in an online environment

Reducing the time spent by experts on the process of cuneiform fragment reconstruction means that more time can be spent on the translation and interpretation of the information that the cuneiform fragments contain. Modern computers and ancillary technologies such as 3D printing have the power to simplify the process of cuneiform reconstruction, and open up the field of reconstruction to non-experts through the use of virtual fragments and new reconstruction methods. In order for computers to be effective in this context, it is important to understand the current state of available technology, and to understand the behaviours and strategies of individuals attempting to reconstruct cuneiform fragments. This thesis presents the results of experiments to determine the behaviours and actions of participants reconstructing cuneiform tablets in the real and virtual world, and then assesses tools developed specifically to facilitate the virtual reconstruction process. The thesis also explores the contemporary and historical state of relevant technologies. The results of experiments show several interesting behaviours and strategies that participants use when reconstructing cuneiform fragments. The experiments include an analysis of the ratio between rotation and movement that show a significant difference between the actions of successful and unsuccessful participants, and an unexpected behaviour that the majority of participants adopted to work with the largest fragments first. It was also observed that the areas of the virtual workspace used by successful participants was different from the areas used by unsuccessful participants. The work further contributes to the field of reconstruction through the development of appropriate tools that have been experimentally proved to dramatically increase the number of potential joins that an individual is able to make over period of time

A Photogrammetric Analysis of Cuneiform Tablets for the purpose of Digital Reconstruction

Despite the advances made in the recording and cataloguing of cuneiform tablets, there is still much work to be done in the field of cuneiform reconstruction. The processes employed to rebuild cuneiform fragments still rely on glue and putty, with manual matching of fragments from catalogues or individual collections. The reconstruction process is hindered by inadequate information about the size and shape of fragments, and the inaccessibility of the original fragments makes finding information difficult in some collections. Most catalogue data associated with cuneiform tablets concerns the content of the text, and not the physical appearance of complete or fragmented tablets. This paper shows how photogrammetric analysis of cuneiform tablets can be used to retrieve physical information directly from source materials without the risk of human error. An initial scan of 8000 images from the CDLI database has already revealed interesting new information about the tablets held in cuneiform archives, and offered new avenues for research within the cuneiform reconstruction process.IBM Visual and Spatial Technology Centre, Institute of Archaeology and Antiquity, University of Birmingham, Edgbaston, Birmingham, B15 2TT

Machine learning for ancient languages: a survey

Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in artificial intelligence and machine learning have enabled analyses on a scale and in a detail that are reshaping the field of humanities, similarly to how microscopes and telescopes have contributed to the realm of science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script, and medium, spanning over three and a half millennia of civilizations around the ancient world. To analyze the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitization, restoration, attribution, linguistic analysis, textual criticism, translation, and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the humanities and machine learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, highlighting promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the humanities and machine learning