TITLE: A Large Dataset of Historical Japanese Documents with Complex Layouts 

ABSTRACT: 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historical documents on a large scale.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
https://dell-research-harvard.github.io/HJDataset/.
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historical documents on a large scale.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
https://dell-research-harvard.github.io/HJDataset/.
A Large Dataset of Historical Japanese Documents with Complex Layouts
http://arxiv.org/abs/2004.08686v1