Robust and high-performance methods for layout analysis in OCR-D

用于 OCR-D 中布局分析的稳健且高性能的方法

• 批准号: 517459941
• 负责人: Professor Dr. Achim Bonte
• 金额: --
• 依托单位: Sächsische Landesbibliothek - Staats- und Universitätsbibliothek
• 依托单位国家: 德国
• 项目类别: Research data and software (Scientific Library Services and Information Systems)
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/517459941?language=en
• 关键词: Robust; performance; methods; layout; analysis

The project aims to improve the quality and robustness of layout analysis for historical documents and thus ensure their aptitude for mass digitization. To achieve this, existing approaches will be optimized and extended, and promising new methods will be integrated. First, a sample-based analysis of the bibliography of books printed in the German-speaking countries in the 16th, 17th and 18th century (VD) will serve to identify (and quantify) those classes of documents for which the results of existing methods for layout analysis are still insufficient. Likewise, suitable training data will be identified and harmonized, and their preparation and generation will be organized more efficiently. The main focus of the work is the further development of complementary methods for layout analysis. On the one hand, a broad coverage for as many documents as possible in the VD is to be achieved by optimizing generic methods and models. On the other hand, this will be complemented by approaches that help to specifically address identified weaknesses by significantly improving the adaptability of the methods and models for new materials and challenges. Furthermore, heuristics are (further) developed in order to optimize the results of different deep learning methods in a rule-based manner. The developments will be accompanied by a detailed evaluation for which scientific standard metrics and tools for layout evaluation will be implemented and integrated in OCR-D, respectively. Last but not least, it must be ensured that all procedures are equipped as modular components with OCR-D interfaces for individual processing steps. This will allow the flexible combination of the procedures to achieve the best possible results and ensure adaptability and sustainability with regard to new developments.

该项目旨在提高历史文献版面分析的质量和稳健性，从而确保其大规模数字化的能力。为了实现这一目标，将对现有方法进行优化和扩展，并将整合有前途的新方法。首先，对16世纪、17世纪和18世纪(VD)德语国家印刷的图书书目进行基于样本的分析，将有助于确定(并量化)现有版面分析方法仍不足以解决的文献类别。同样，将确定和协调适当的培训数据，并将更有效地组织这些数据的编制和生成。这项工作的主要重点是进一步发展布局分析的补充方法。一方面，要通过优化通用方法和模型来实现对VD中尽可能多的文件的广泛覆盖。另一方面，将通过显著提高方法和模式对新材料和挑战的适应性，帮助具体解决已查明的弱点的办法加以补充。此外，为了以基于规则的方式优化不同深度学习方法的结果，(进一步)开发了启发式算法。这些发展将伴随着详细的评估，其中将分别在OCR-D中实施和整合布局评估的科学标准度量和工具。最后但并非最不重要的一点是，必须确保所有程序都作为模块化组件配备，带有用于各个处理步骤的OCR-D接口。这将使程序的灵活组合能够取得最好的结果，并确保对新的事态发展的适应性和可持续性。