# A Large Dataset of Historical Japanese Documents with Complex Layouts

arXiv: https://arxiv.org/abs/2004.08686 · HTML: https://mlsanity.com/paper/2004.08686/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 25 of 25)

- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer](https://mlsanity.com/paper/2201.11438/) (2022)
- [SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation](https://mlsanity.com/paper/2305.04609/) (2023)
- [RoDLA: Benchmarking the Robustness of Document Layout Analysis Models](https://mlsanity.com/paper/2403.14442/) (2024)
- [Efficient OCR for Building a Diverse Digital History](https://mlsanity.com/paper/2304.02737/) (2023)
- [SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation](https://mlsanity.com/paper/2305.00795/) (2023)
- [Digital Peter: Dataset, Competition and Handwriting Recognition Methods](https://mlsanity.com/paper/2103.09354/) (2021)
- [EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge](https://mlsanity.com/paper/2310.10050/) (2023)
- [Processing the structure of documents: Logical Layout Analysis of historical newspapers in French](https://mlsanity.com/paper/2202.08125/) (2022)
- [DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning](https://mlsanity.com/paper/2504.04085/) (2025)
- [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026)
- [Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections](https://mlsanity.com/paper/2305.14588/) (2023)
- [GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation](https://mlsanity.com/paper/2402.11401/) (2024)
- [UnSupDLA: Towards Unsupervised Document Layout Analysis](https://mlsanity.com/paper/2406.06236/) (2024)
- [Multimodal LLMs for Historical Dataset Construction from Archival Image Scans: German Patents (1877-1918)](https://mlsanity.com/paper/2512.19675/) (2025)
- [Cross-Domain Document Layout Analysis Using Document Style Guide](https://mlsanity.com/paper/2201.09407/) (2022)
- [Title block detection and information extraction for enhanced building drawings search](https://mlsanity.com/paper/2504.08645/) (2025)
- [Towards Khmer Scene Document Layout Detection](https://mlsanity.com/paper/2603.00707/) (2026)
- [Document Layout Analysis with Aesthetic-Guided Image Augmentation](https://mlsanity.com/paper/2111.13809/) (2021)
- [Using maps to predict economic activity](https://mlsanity.com/paper/2112.13850/) (2021)
- [HybriDLA: Hybrid Generation for Document Layout Analysis](https://mlsanity.com/paper/2511.19919/) (2025)
- [Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation](https://mlsanity.com/paper/2605.25781/) (2026)
- [Complex Layout Classification in the Wild: A Low-Resource Approach with Layout-Preserving Augmentations](https://mlsanity.com/paper/2606.17355/) (2026)
- [PalmLeaf-VQA: A Multi-Script Visual Question Answering Benchmark for Historical Palm-Leaf Manuscript Understanding Across Diverse Regions](https://mlsanity.com/paper/2609.31651/) (2026)

## Abstract (arXiv)

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/.