Barak Sober

Algorithmic handwriting analysis of Judah's military correspondence sheds light on composition of biblical texts.

Significance Scholars debate whether the first major phase of compilation of biblical texts took place before or after the destruction of Jerusalem in 586 BCE. Proliferation of literacy is considered a precondition for the creation of such texts. Ancient inscriptions provide important evidence of the proliferation of literacy. This paper focuses on 16 ink inscriptions found in the desert fortress of Arad, written ca. 600 BCE. By using novel image processing and machine learning algorithms we deduce the presence of at least six authors in this corpus. This indicates a high degree of literacy in the Judahite administrative apparatus and provides a possible stage setting for compilation of biblical texts. After the kingdom’s demise, a similar literacy level reemerges only ca. 200 BCE. The relationship between the expansion of literacy in Judah and composition of biblical texts has attracted scholarly attention for over a century. Information on this issue can be deduced from Hebrew inscriptions from the final phase of the first Temple period. We report our investigation of 16 inscriptions from the Judahite desert fortress of Arad, dated ca. 600 BCE—the eve of Nebuchadnezzar’s destruction of Jerusalem. The inquiry is based on new methods for image processing and document analysis, as well as machine learning algorithms. These techniques enable identification of the minimal number of authors in a given group of inscriptions. Our algorithmic analysis, complemented by the textual information, reveals a minimum of six authors within the examined inscriptions. The results indicate that in this remote fort literacy had spread throughout the military hierarchy, down to the quartermaster and probably even below that rank. This implies that an educational infrastructure that could support the composition of literary texts in Judah already existed before the destruction of the first Temple. A similar level of literacy in this area is attested again only 400 y later, ca. 200 BCE.

Reconstructing Ancient Israel: Integrating Macro- and Micro-archaeology

The study of ancient Israel’s texts and history has been a keystone of European scholarship since the Enlightenment. From the beginning of the 19th century, biblical exegesis contributed impressively to our understanding of these topics. Biblical archaeology joined in about a century later and provided critical evidence for the material culture of ancient Israel, shedding new light on its history. Yet, until recent years (and in certain circles up until today) biblical archaeology was dominated by a conservative interpretation of the texts and was not given a true independent role in recon-

MULTISPECTRAL IMAGING AS A TOOL FOR ENHANCING THE READING OF OSTRACA

Abstract A recent study shows that multispectral (MS) imaging can improve the legibility of ostraca. Several examples of Iron Age ostraca unearthed over twenty years ago in Israel (Ḥorvat ʿUza and Ḥorvat Radum) are presented, showing how new images taken with an MS system improve the reading of inscriptions that have significantly faded over time. The article provides instructions for constructing a simple and low-cost MS imaging system that yields comparable results to commercial systems.1

Potential Contrast – A New Image Quality Measure

This paper suggests a new quality measure of an image, pertaining to its contrast. Several contrast measures exist in the current research. However, due to the abundance of Image Processing software solutions, the perceived (or measured) image contrast can be misleading, as the contrast may be significantly enhanced by applying grayscale transformations. Therefore, the real challenge, which was not dealt with in the previous literature, is measuring the contrast of an image taking into account all possible grayscale transformations, leading to the best “potential” contrast. Hence, we suggest an alternative “Potential Contrast” measure, based on sampled populations of foreground and background pixels (e.g. scribbles or saliency-based criteria). An exact and efficient implementation of this measure is found analytically. The new methodology is tested and is shown to be invariant to invertible grayscale transformations. Introduction Establishing the contrast of an image is a well-studied problem in the fields of Optics and Image Processing. Several measures have been proposed, for that purpose, in the past. Among these are the contrast measures of Weber [1], Michelson [1, 2], root-mean-square contrast and its enhancements [3,4], CMI [5-8], as well as measures based on frequency domain analysis [1,9], wavelet transforms [9,10] and edge detection [11,12]. However, the problem is complicated by the immense set of transformations which can be applied to the image, potentially improving its contrast. Given a proliferation of the available Image Processing software solutions, applying such enhancements is almost indispensable. Therefore, the real challenge, which was not dealt with in the previous literature, is measuring the contrast of an image taking into account all its possible transformations. In this article, we will limit ourselves to the wide range of grayscale transformations. Prior Art Various algorithms were designed to give an objective contrast measure that correlates with human assessment. In what follows, we consider grayscale images of the form       : 1, 1, 0,255 I L M   (unless stated otherwise, throughout the article, the intervals are assumed to be subsets of integers). We review several popular contrast measures, stating their relative shortcomings. A simple way of measuring a bi-population image contrast is calculating the ratio between foreground and background: : / B F SimpleContrast    (1) where B  and F  are the averages of the sampled background and foreground luminance values, respectively. A more commonly used measure (closely related to SimpleContrast ) is Webers contrast ratio [1] defined as:

Computer aided restoration of handwritten character strokes

This work suggests a new variational approach to the task of computer aided restoration of incomplete characters, residing in a highly noisy document. We model character strokes as the movement of a pen with a varying radius. Following this model, a cubic spline representation is being utilized to perform gradient descent steps, while maintaining interpolation at some initial (manually sampled) points. The proposed algorithm was utilized in the process of restoring approximately 1000 ancient Hebrew characters (dating to ca. 8th-7th century BCE), some of which are presented herein and show that the algorithm yields plausible results when applied on deteriorated documents.

A Brand New Old Inscription: Arad Ostracon 16 Rediscovered via Multispectral Imaging

Arad Ostracon 16 is part of the Elyashiv Archive, dated to ca. 600 B.C. It was published as bearing an inscription on the recto only. New multispectral images of the ostracon have enabled us to reveal a hitherto invisible inscription on the verso, as well as additional letters, words, and complete lines on the recto. We present here the new images and offer our new reading and reinterpretation of the ostracon.

Beyond the Ground Truth: Alternative Quality Measures of Document Binarizations

This article discusses the quality assessment of binary images. The customary, ground truth based methodology, used in the literature is shown to be problematic due to its subjective nature. Several previously suggested alternatives are surveyed and are also found to be inadequate in certain scenarios. A new approach, quantifying the adherence of a binarization to its document image is proposed and tested using six different measures of accuracy. The measures are evaluated experimentally based on datasets from DIBCO and H-DIBCO competitions, with respect to different kinds of binarization degradations.