历史文献文本行提取与手写识别的组合系统

Andreas Fischer, M. Baechler, A. Garz, M. Liwicki, R. Ingold
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引用次数: 24

摘要

在数字图书馆中,基于文本内容搜索和浏览古代手稿需要自动阅读历史笔迹。在本文中,我们提出了一种用于页面图像文本定位和转录的组合系统。它包括灵活的基于学习的布局分析和手写识别方法,这些方法是在瑞士研究项目HisDoc的背景下开发的。对中世纪Parzival数据库进行了全面的实验评估,结果表明,在封闭词汇情况下,单词识别准确率达到93.0%。为了协调两种文档分析任务的评估,我们引入了一种考虑替换、删除和插入错误的文本行提取评估方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Combined System for Text Line Extraction and Handwriting Recognition in Historical Documents
Automated reading of historical handwriting is needed to search and browse ancient manuscripts in digital libraries based on their textual content. In this paper, we present a combined system for text localization and transcription in page images. It includes flexible learning-based methods for layout analysis and handwriting recognition, which were developed in the context of the Swiss research project HisDoc. A comprehensive experimental evaluation is provided for the medieval Parzival database, demonstrating a promising word recognition accuracy of 93.0% with closed vocabulary. In order to harmonize the evaluation of the two document analysis tasks, we introduce a novel evaluation measure for text line extraction that takes substitution, deletion, as well as insertion errors into account.
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