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 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

Automatic Script Identification from Images Using Cluster-based Templates

We describe a system that automatically identifies the script used in documents stored electronically in image form. The system can learn to distinguish any number of scripts. It develops a set of representative symbols (templates) for each script by clustering textual symbols from a set of training documents and representing each cluster by its centroid. "Textual symbols" include discrete characters in scripts such as Cyrillic, as well as adjoined characters, character fragments, and whole words in connected scripts such as Arabic. To identify a new document 's script, the system compares a subset of symbols from the document to each script's templates, screening out rare or unreliable templates, and choosing the script whose templates provide the best match. Our current system, trained on thirteen scripts, correctly identifies all test documents except those printed in fonts that differ markedly from fonts in the training set. 1. Introduction Script identification is a key part of th..

Pattern detection and recognition using over-complete and sparse representations

Recent research in harmonic analysis and mammalian vision systems has revealed that over-complete and sparse representations play an important role in visual information processing. The research on applying such representations to pattern recognition and detection problems has become an interesting field of study. The main contribution of this thesis is to propose two feature extraction strategies - the global strategy and the local strategy - to make use of these representations. In the global strategy, over-complete and sparse transformations are applied to the input pattern as a whole and features are extracted in the transformed domain. This strategy has been applied to the problems of rotation invariant texture classification and script identification, using the Ridgelet transform. Experimental results have shown that better performance has been achieved when compared with Gabor multi-channel filtering method and Wavelet based methods. The local strategy is divided into two stages. The first one is to analyze the local over-complete and sparse structure, where the input 2-D patterns are divided into patches and the local over-complete and sparse structure is learned from these patches using sparse approximation techniques. The second stage concerns the application of the local over-complete and sparse structure. For an object detection problem, we propose a sparsity testing technique, where a local over-complete and sparse structure is built to give sparse representations to the text patterns and non-sparse representations to other patterns. Object detection is achieved by identifying patterns that can be sparsely represented by the learned. structure. This technique has been applied. to detect texts in scene images with a recall rate of 75.23% (about 6% improvement compared with other works) and a precision rate of 67.64% (about 12% improvement). For applications like character or shape recognition, the learned over-complete and sparse structure is combined. with a Convolutional Neural Network (CNN). A second text detection method is proposed based on such a combination to further improve (about 11% higher compared with our first method based on sparsity testing) the accuracy of text detection in scene images. Finally, this method has been applied to handwritten Farsi numeral recognition, which has obtained a 99.22% recognition rate on the CENPARMI Database and a 99.5% recognition rate on the HODA Database. Meanwhile, a SVM with gradient features achieves recognition rates of 98.98% and 99.22% on these databases respectivel

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