Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)

• URL: http://arxiv.org/abs/2010.14476v1
• Date: Tue, 27 Oct 2020 17:36:18 GMT
• Title: Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)
• Authors: Mladen Popovi\'c, Maruf A. Dhali, Lambert Schomaker
• Abstract summary: We use pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls regarding writer identification. Although many scholars believe that 1QIsaa was written by one scribe, we report new evidence for a breaking point in the series of columns in this scroll. This study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes could closely collaborate on one particular manuscript.
• Score: 5.285396202883411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. However, one of the problems of traditional palaeography is to determine writer identity when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we used pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls regarding writer identification and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. Although many scholars believe that 1QIsaa was written by one scribe, we report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature-space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent around column 27. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed. Demonstrating that two main scribes were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes could closely collaborate on one particular manuscript.

Related papers

• Critical biblical studies via word frequency analysis: unveiling text authorship [7.2762881851201255]
  We aim to differentiate between three distinct authors across numerous chapters spanning the first nine books of the Bible. Our analysis indicates that the first two authors (D and DtrH) are much more closely related compared to P, a fact that aligns with expert assessments.
  arXiv  Detail & Related papers  (2024-10-24T22:08:38Z)
• Dating ancient manuscripts using radiocarbon and AI-based writing style analysis [1.828413418929518]
  We present Enoch, an AI-based date-prediction model, trained on the basis of new radiocarbon-dated samples of the Dead Sea Scrolls. Enoch could predict the radiocarbon-based dates from style, supported by leave-one-out validation, with varied MAEs of 27.9 to 30.7 years relative to the radiocarbon dating. It was then used to estimate the dates of 135 unseen manuscripts, revealing that 79 per cent of the samples were considered'realistic' upon palaeographic post-hoc evaluation.
  arXiv  Detail & Related papers  (2024-06-26T12:33:34Z)
• Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction [73.26364649572237]
  Oracle Bone Inscriptions is one of the oldest existing forms of writing in the world. A large number of Oracle Bone Inscriptions (OBI) remain undeciphered, making it one of the global challenges in paleography today. This paper introduces a novel approach, namely Puzzle Pieces Picker (P$3$), to decipher these enigmatic characters through radical reconstruction.
  arXiv  Detail & Related papers  (2024-06-05T07:34:39Z)
• From exemplar to copy: the scribal appropriation of a Hadewijch manuscript computationally explored [1.9798034349981157]
  This study is devoted to two of the oldest known manuscripts in which the oeuvre of the medieval mystical author Hadewijch has been preserved. It is assumed that the scribe who produced B used A as an exemplar. We use machine learning to identify the most distinctive features that separate manuscript A from B.
  arXiv  Detail & Related papers  (2022-10-25T14:40:25Z)
• Computer-Aided Modelling of the Bilingual Word Indices to the Ninth-Century Uchitel'noe evangelie [0.0]
  We show how we model various types of asymmetric translation correlates and the variability resulting from the pluralism of sources. Our approach is designed with generalisation in mind and is intended to be applicable also for other translations from Greek into Old Church Slavonic.
  arXiv  Detail & Related papers  (2022-10-25T10:16:39Z)
• Unsupervised Clustering of Roman Potsherds via Variational Autoencoders [63.8376359764052]
  We propose an artificial intelligence solution to support archaeologists in the classification task of Roman commonware potsherds. The partiality and handcrafted variance of the fragments make their matching a challenging problem. We propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network.
  arXiv  Detail & Related papers  (2022-03-14T18:56:13Z)
• Letter-level Online Writer Identification [86.13203975836556]
  We focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles)
  arXiv  Detail & Related papers  (2021-12-06T07:21:53Z)
• Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification [15.010153819096056]
  In this paper, three different deep learning techniques - spatial attention mechanism, multi-scale feature fusion and patch-based CNN were proposed to capture the difference between each writer's handwriting. The proposed methods outperforms various state-of-the-art methodologies on word-level and page-level writer identification methods on three publicly available datasets.
  arXiv  Detail & Related papers  (2021-11-20T14:41:36Z)
• Image Collation: Matching illustrations in manuscripts [76.21388548732284]
  We introduce the task of illustration collation and a large annotated public dataset to evaluate solutions. We analyze state of the art similarity measures for this task and show that they succeed in simple cases but struggle for large manuscripts. We show clear evidence that significant performance boosts can be expected by exploiting cycle-consistent correspondences.
  arXiv  Detail & Related papers  (2021-08-18T12:12:14Z)
• Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals [69.76097138157816]
  Probabilistic text generators have been used to produce fake scientific papers for more than a decade. Complex AI-powered generation techniques produce texts indistinguishable from that of humans. Some websites offer to rewrite texts for free, generating gobbledegook full of tortured phrases.
  arXiv  Detail & Related papers  (2021-07-12T20:47:08Z)
• How to Train Your Agent to Read and Write [52.24605794920856]
  Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. We propose a Deep ReAder-Writer (DRAW) network, which consists of a textitReader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text textitWriter that generates a novel paragraph, and a textit
  arXiv  Detail & Related papers  (2021-01-04T12:22:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.