Deep learning-based approaches for automatic document layout analysis and
content extraction have the potential to unlock rich information trapped in
historical documents on a large scale. One major hurdle is the lack of large
datasets for training robust models. In particular, little training data exist
for Asian languages. To this end, we present HJDataset, a Large Dataset of
Historical Japanese Documents with Complex Layouts. It contains over 250,000
layout element annotations of seven types. In addition to bounding boxes and
masks of the content regions, it also includes the hierarchical structures and
reading orders for layout elements. The dataset is constructed using a
combination of human and machine efforts. A semi-rule based method is developed
to extract the layout elements, and the results are checked by human
inspectors. The resulting large-scale dataset is used to provide baseline
performance analyses for text region detection using state-of-the-art deep
learning models. And we demonstrate the usefulness of the dataset on real-world
document digitization tasks. The dataset is available at
this https URL

本文介绍了一种使用 HJDataset 的深度学习方法，可以准确地分析历史日文文档的布局并提取内容。该数据集包含 250,000 多个布局元素注释和基于半规则的方法构建，旨在提供深度学习模型的基准性能分析。