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Feature-extraction methods for historical manuscript dating based on writing style development

机译:基于写作风格开发的历史稿稿的特征提取方法

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摘要

Paleographers and philologists perform significant research in finding the dates of ancient manuscripts to understand the historical contexts. To estimate these dates, the traditional process of using classical paleography is subjective, tedious, and often time-consuming. An automatic system based on pattern recognition techniques that infers these dates would be a valuable tool for scholars. In this study, the development of handwriting styles over time in the Dead Sea Scrolls, a collection of ancient manuscripts, is used to create a model that predicts the date of a query manuscript. In order to extract the handwriting styles, several dedicated feature-extraction techniques have been explored. Additionally, a self-organizing time map is used as a codebook. Support vector regression is used to estimate a date based on the feature vector of a manuscript. The date estimation from grapheme-based technique outperforms other feature-extraction techniques in identifying the chronological style development of handwriting in this study of the Dead Sea Scrolls. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
机译:很着古学家和理发师在寻找古代手稿的日期来了解历史背景的重要研究。为了估算这些日期,使用古典古典的传统过程是主观的,乏味,往往耗时。一种基于模式识别技术的自动系统,即越过这些日期是学者的宝贵工具。在这项研究中,在死海滚动中随着时间的推移发展,古代稿件的集合,用于创建一个预测查询稿件日期的模型。为了提取手写样式,已探讨了几种专用特征提取技术。此外,自组织时间映射用作码本。支持向量回归用于基于手稿的特征向量估计日期。基于Graphy的技术的日期估计优于识别死海滚动研究中手写的按时间提取技术的其他特征提取技术。 (c)2020作者。由elsevier b.v发布。这是CC By-NC-ND许可下的开放式访问文章。 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

著录项

  • 来源
    《Pattern recognition letters》 |2020年第3期|413-420|共8页
  • 作者单位

    Univ Groningen Bernoulli Inst Dept Artificial Intelligence NL-9747 AG Groningen Netherlands;

    Univ Groningen Bernoulli Inst Dept Artificial Intelligence NL-9747 AG Groningen Netherlands;

    Univ Groningen Bernoulli Inst Dept Artificial Intelligence NL-9747 AG Groningen Netherlands;

    Univ Groningen Bernoulli Inst Dept Artificial Intelligence NL-9747 AG Groningen Netherlands;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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