A Large Dataset of Historical Japanese Documents with Complex Layouts
| العنوان: | A Large Dataset of Historical Japanese Documents with Complex Layouts |
|---|---|
| المؤلفون: | Kaixuan Zhang, Melissa Dell, Zejiang Shen |
| المصدر: | CVPR Workshops |
| بيانات النشر: | IEEE, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Information retrieval, Training set, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, Deep learning, Computer Science - Computer Vision and Pattern Recognition, 020207 software engineering, 02 engineering and technology, Reading (process), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Scale (map), business, Baseline (configuration management), Document layout analysis, Digitization, media_common |
| الوصف: | 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for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale.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/. 8 pages, 8 figures, accepted at CVPR2020 Workshop on Text and Documents in the Deep Learning Era |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24f7e9183b9e83dcbf9c2961bc42f1cb https://doi.org/10.1109/cvprw50498.2020.00282 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....24f7e9183b9e83dcbf9c2961bc42f1cb |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |