Background. In recent years, libraries and archives led important
digitisation campaigns that opened the access to vast collections of historical
documents. While such documents are often available as XML ALTO documents, they
lack information about their logical structure. In this paper, we address the
problem of Logical Layout Analysis applied to historical documents in French.
We propose a rule-based method, that we evaluate and compare with two
Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set
contains French newspapers, periodicals and magazines, published in the first
half of the twentieth century in the Franche-Comté Region. Results. Our
rule-based system outperforms the two other models in nearly all evaluations.
It has especially better Recall results, indicating that our system covers more
types of every logical label than the other two models. When comparing RIPPER
with Gradient Boosting, we can observe that Gradient Boosting has better
Precision scores but RIPPER has better Recall scores. Conclusions. The
evaluation shows that our system outperforms the two Machine Learning models,
and provides significantly higher Recall. It also confirms that our system can
be used to produce annotated data sets that are large enough to envisage
Machine Learning or Deep Learning approaches for the task of Logical Layout
Analysis. Combining rules and Machine Learning models into hybrid systems could
potentially provide even better performances. Furthermore, as the layout in
historical documents evolves rapidly, one possible solution to overcome this
problem would be to apply Rule Learning algorithms to bootstrap rule sets
adapted to different publication periods.

本文探讨了应用于法国历史文件的逻辑版面分析问题，并提出了一种基于规则的方法，并对两种机器学习模型 RIPPER 和 Gradient Boosting 进行了评估和比较。通过实验表明，我们的规则系统表现优于两种机器学习模型，提高了召回率，并证实了我们的系统可以生成足够大的注释数据集，以便于机器学习或深度学习方法进行逻辑版面分析任务。