将大型历史公证手稿分割为多页契约

IF 3.7 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jose Ramón Prieto, David Becerra, Alejandro Hector Toselli, Carlos Alonso, Enrique Vidal
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引用次数: 0

摘要

世界各地的档案馆拥有大量数字化的历史手稿书籍或 "包",其中包括公证记录,也称为 "契约 "或 "行为"。要提供描述这些书卷内容的元数据,首要步骤之一就是将它们分割成单个契约。即使契约通常都是页面对齐的,就像本作品中考虑的包一样,这也是一项耗时的任务,而且由于所涉及的手稿系列规模巨大,这项任务往往令人望而却步。与用于页面级分割的传统布局分析方法不同,我们的方法超越了单页图像的范畴,可为整卷手稿提供一致的契据检测结果。这是通过两个紧密结合的步骤实现的:首先,我们在页面级别估计 "初始"、"中间 "和 "最终 "类别的类别后设;然后,我们应用一系列顺序一致性约束对这些后设进行 "解码",以获得一致的书籍分割。我们对四部大型历史手稿进行了实验,并改变了用于训练的 "契约 "数量。实验引入了两个指标来评估书籍分割的质量,其中一个指标考虑到了分割错误带来的信息损失。问题的形式化、度量和实证工作大大扩展了我们之前在这一主题上的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Segmenting large historical notarial manuscripts into multi-page deeds

Segmenting large historical notarial manuscripts into multi-page deeds

Archives around the world hold vast digitized series of historical manuscript books or “bundles” containing, among others, notarial records also known as “deeds” or “acts”. One of the first steps to provide metadata which describe the contents of those bundles is to segment them into their individual deeds. Even if deeds are often page-aligned, as in the bundles considered in the present work, this is a time-consuming task, often prohibitive given the huge scale of the manuscript series involved. Unlike traditional Layout Analysis methods for page-level segmentation, our approach goes beyond the realm of a single-page image, providing consistent deed detection results on full bundles. This is achieved in two tightly integrated steps: first, we estimate the class-posterior at the page level for the “initial”, “middle”, and “final” classes; then we “decode” these posteriors applying a series of sequentiality consistency constraints to obtain a consistent book segmentation. Experiments are presented for four large historical manuscripts, varying the number of “deeds” used for training. Two metrics are introduced to assess the quality of book segmentation, one of them taking into account the loss of information entailed by segmentation errors. The problem formalization, the metrics and the empirical work significantly extend our previous works on this topic.

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来源期刊
Pattern Analysis and Applications
Pattern Analysis and Applications 工程技术-计算机:人工智能
CiteScore
7.40
自引率
2.60%
发文量
76
审稿时长
13.5 months
期刊介绍: The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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