| --- |
| license: cc-by-sa-4.0 |
|
|
| language: |
| - jp |
| tags: |
| - historical-documents |
| - ocr |
| - layout-analysis |
| - mantetsu |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Early 20th Century Historical Document OCR Dataset |
|
|
| This is a large-scale, manually annotated image dataset specifically designed for early historical documents from the last century. It contains relevant historical literature from that period, complete with Optical Character Recognition (OCR) annotations. |
|
|
| Notably, the dataset is divided into two main parts: |
| * **Simulated Image Folder:** Tailored for training restoration diffusion models. It contains only Ground Truth (GT) and Low-Quality (LQ) images (i.e., clear and degraded images) without text annotations. |
| * **MTJ_OCR Data:** Contains the original scanned images along with their corresponding annotation files. |
| |
| ## Dataset Details |
| |
| ### Dataset Description |
| |
| This dataset aims to address the complex layout recognition challenges encountered during the digitization of early historical documents. Because archives from the last century feature extensive Traditional Chinese, old-style Japanese (including obsolete Kanji), complex statistical tables, and special seals, this dataset provides a valuable benchmark environment for Document AI research in the fields of Computer Vision (CV) and Digital Humanities. |
| |
| ### Dataset Sources |
| - **Repository:** [https://huggingface.co/datasets/corste1/MTJ-OCR](https://huggingface.co/datasets/corste1/MTJ-OCR) |
| |
| ## Uses |
| |
| ### Direct Use |
| - **OCR Training:** Character-level or line-level recognition targeting Traditional Chinese, variant characters, and old-style Japanese in vintage printed materials. |
| |
| ### Out-of-Scope Use |
| - This dataset must not be used to forge historical documents or maliciously tamper with information in the original scans. |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| Due to the unique layout styles of early East Asian literature (e.g., multi-column mixed layouts, vertical text, mixed text and graphics), paper aging, and cross-lingual characteristics, general-purpose modern OCR models often perform poorly. By manually annotating this large-scale dataset, we can significantly enhance machine understanding and digitization capabilities for complex early documents. |
| |
| ### Source Data |
| |
| #### Data Collection and Processing |
| - **Source:** Scanned from original investigation reports, statistical yearbooks, and official archival documents from the first half of the 20th century. |
| |
| #### Who are the source data producers? |
| The original data originated from various institutional publications and archives produced between the early and mid-to-late 20th century (approx. 1940s-1980s). |
| |
| ## Bias, Risks, and Limitations |
| |
| - **Image Quality Limitations:** Due to the printing techniques of the time and long-term storage conditions, some early archives suffer from physical defects such as ink bleed-through, blurred text, or torn paper. These factors increase the recognition difficulty for computer vision models. |
| - **Linguistic and Textual Features:** The text within the dataset reflects the specific linguistic habits and typesetting norms of its era. Models trained on this dataset may require fine-tuning when processing modern, standard text. |
| |
| ## Citation |
| |
| If you use this dataset in your research, please cite it as follows: |
| |
| **BibTeX:** |
| |
| ```bibtex |
| @dataset{historical_document_ocr_2026, |
| author = {YONG ZHENG}, |
| title = {Early 20th Century Historical Document OCR Dataset}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {[https://huggingface.co/datasets/corste1/MTJ-OCR](https://huggingface.co/datasets/corste1/MTJ-OCR)} |
| } |
