| --- |
| language: |
| - en |
| - fr |
| - es |
| license: mit |
| size_categories: |
| - n<1K |
| task_categories: |
| - object-detection |
| - image-segmentation |
| tags: |
| - pdf |
| - document-layout-analysis |
| - data-extraction |
| configs: |
| - config_name: annotations |
| data_files: |
| - split: unhcr |
| path: annotations/unhcr/*.json |
| - split: prwp |
| path: annotations/prwp/*.json |
| - split: refugee |
| path: annotations/refugee/*.json |
| - config_name: metadata |
| data_files: |
| - split: unhcr |
| path: metadata/unhcr/*.json |
| - split: prwp |
| path: metadata/prwp/*.json |
| - split: refugee |
| path: metadata/refugee/*.json |
| - config_name: documents |
| data_files: |
| - split: unhcr |
| path: documents/unhcr/*.pdf |
| - split: prwp |
| path: documents/prwp/*.pdf |
| - split: refugee |
| path: documents/refugee/*.pdf |
| - config_name: snapshots |
| data_files: |
| - split: unhcr |
| path: snapshots/unhcr/*.png |
| - split: prwp |
| path: snapshots/prwp/*.png |
| - split: refugee |
| path: snapshots/refugee/*.png |
| --- |
| |
| # Dataset card for data-snapshot |
|
|
| This dataset was introduced in the paper [Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents](https://huggingface.co/papers/2606.06242). |
|
|
| The source code for the benchmark and dataset extraction is available on GitHub: [worldbank/ai4data](https://github.com/worldbank/ai4data/tree/main/experimental/data-snapshot). |
|
|
| ## Dataset summary |
| The `data-snapshot` dataset is an annotated corpus designed for the evaluation and development of models for extracting *data snapshots* from PDF documents. A **data snapshot** is defined as a figure or table containing structured or semi-structured information intended for analytical interpretation or operational reuse. |
|
|
| The benchmark spans humanitarian reports, World Bank policy research working papers, and project appraisal documents, and includes annotations for figures and tables that contain reusable analytical information. |
|
|
| ## Dataset structure |
|
|
| The repository is organized as follows: |
|
|
| ``` |
| ai4data/data-snapshot/ |
| ├── annotations/<source>/*.json # Contains annotation files per document |
| ├── documents/<source>/*.pdf # Actual PDFs |
| ├── metadata/<source>/*.json # Document-level metadata |
| ├── schemas/*.json # Provides the schema of the annotation and metadata files |
| ├── snapshots/<source>/*.png # Image files corresponding to the annotations |
| └── README.md |
| ``` |
|
|
| ### Subsets |
| - `annotations` |
| - JSON files that indicate the data snapshots: their object class (Figure / Table) and bounding box locations (in normalized `[x1, y1, x2, y2]` format, top-left origin) |
| - Follows the schema provided in `schemas/data-snapshot-eval-v1.3.schema.json` |
| - Provided on a per-document basis; documents that do not have data snapshots will still have an annotation file present but list of bounding boxes will be empty. |
| - `documents` |
| - Actual PDF files that were annotated |
| - `metadata` |
| - Document-level metadata following the [World Bank Metadata Standards (Chapter 5 — Documents)](https://worldbank.github.io/schema-guide/chapter05.html), schema provided in `schemas/metadata_schema.json`. |
| - Provided on a per-document basis |
| - All files across all sources share a uniform schema (same keys at every nesting level) |
| - `snapshots` |
| - PNG files extracted from the documents and bounding box locations |
|
|
| ### Sources |
| - UNHCR |
| - PRWP |
| - Refugee |
|
|
| ## Loading the dataset using HF's `datasets` library |
|
|
| ### Annotations |
|
|
| ```python |
| >>> from datasets import load_dataset |
| >>> annotations = load_dataset("ai4data/data-snapshot", name="annotations", split="unhcr") |
| >>> annotations[0] # Inspect the first record |
| {'label_map': {'1': 'Figure', '2': 'Table'}, 'info': {'schema_version': '1.3', 'type': 'ground_truth', 'created_at': datetime.datetime(2026, 5, 20, 13, 44, 29), 'run_id': 'human-annotation-combined-e3432dce89', 'model': {'name': 'human annotation'}, 'coordinate_system': {'type': 'normalized_xyxy', 'range': [0.0, 1.0], 'origin': 'top_left'}}, 'documents': [{'doc_id': '06072015-baalbek-hermelgovernorateprofile.pdf', 'doc_name': '06072015-baalbek-hermelgovernorateprofile.pdf', 'doc_path': 'pdf_input/06072015-baalbek-hermelgovernorateprofile.pdf'}], 'predictions': [{'page_id': '06072015-baalbek-hermelgovernorateprofile.pdf::p000', 'doc_id': '06072015-baalbek-hermelgovernorateprofile.pdf', 'page_index': 0, 'objects': [{'id': '1d69f693', 'label': 'Figure', 'bbox': [0.029415499554572243, 0.1766403810171256, 0.5954839424856321, 0.7354445202645015], 'score': None}, ...} |
| ``` |
|
|
| ### Metadata |
|
|
| ```python |
| >>> metadata = load_dataset("ai4data/data-snapshot", name="metadata", split="unhcr") |
| >>> metadata[0] # Inspect the first record |
| {'type': 'document', 'metadata_information': {'title': 'Lebanon: Baalbek-Hermel Governorate Profile (June 2015)', 'idno': '06072015-baalbek-hermelgovernorateprofile', 'producers': [{'name': 'UNHCR', 'abbr': 'UNHCR', 'affiliation': 'UNHCR', 'role': 'Source'}], 'production_date': datetime.datetime(2026, 5, 21, 0, 0), ...} |
| ``` |
|
|
| ### Documents |
|
|
| ```python |
| >>> docs = load_dataset("ai4data/data-snapshot", data_dir="documents/unhcr") # Or simply data_dir="documents/" for all files |
| >>> docs.save_to_disk("path/to/docs_directory") # Files are saved as an Arrow file |
| ``` |
|
|
| ### Snapshots |
|
|
| ```python |
| >>> snapshots = load_dataset("ai4data/data-snapshot", data_dir="snapshots/unhcr") # Or simply data_dir="snapshots/" for all snapshots |
| >>> snapshots.save_to_disk("path/to/snapshots_directory") # Files are saved as an Arrow file |
| ``` |
|
|
| ## Schema |
|
|
| ### Annotations |
|
|
| The annotation files follow the **Data Snapshot Evaluation Format (v1.3)**. Below is a simplified, human-readable example of the JSON schema with explanatory comments for each field. |
|
|
| > **Note**: You will notice a top-level field called `predictions`. In the context of this dataset, this is a misnomer because these are actually human-labeled **annotations** (ground truth). We use the key `predictions` because we borrow this schema from the project's evaluation codebase, which uses a unified structure for both ground truth and model predictions. |
|
|
| ```json |
| { |
| // Canonical mapping of integer IDs to class names |
| "label_map": { |
| "1": "Figure", |
| "2": "Table" |
| }, |
| |
| // High-level metadata about the file |
| "info": { |
| "schema_version": "1.3", |
| "type": "ground_truth", // Indicates these are human annotations |
| "created_at": "2026-05-20T13:44:29", |
| "run_id": "human-annotation-combined-e3432dce89", |
| "model": { |
| "name": "human annotation" |
| }, |
| "coordinate_system": { |
| "type": "normalized_xyxy", |
| "range": [0.0, 1.0], // Bounding boxes are normalized between 0 and 1 |
| "origin": "top_left" |
| } |
| }, |
| |
| // List of documents referenced in this file |
| "documents": [ |
| { |
| "doc_id": "1_advocacy_note_mineaction_-_niger_eng.pdf", |
| "doc_name": "1_advocacy_note_mineaction_-_niger_eng.pdf", |
| "doc_path": "pdf_input/1_advocacy_note_mineaction_-_niger_eng.pdf" |
| } |
| ], |
| |
| // Per-page container of objects; these contain the ground truth annotations |
| "predictions": [ |
| { |
| "page_id": "1_advocacy_note_mineaction_-_niger_eng.pdf::p001", |
| "doc_id": "1_advocacy_note_mineaction_-_niger_eng.pdf", |
| "page_index": 0, // 0-indexed page number |
| "objects": [ |
| { |
| "id": "obj_001", |
| "label": "Figure", // Matches a label_map entry |
| "bbox": [0.029, 0.177, 0.595, 0.735], // Normalized [x_min, y_min, x_max, y_max] |
| "score": null // Always null for ground truth |
| } |
| ] |
| } |
| ] |
| } |
| ``` |
|
|
| ### Metadata |
|
|
| The metadata files follow the [**World Bank Document Metadata Schema**](https://worldbank.github.io/schema-guide/chapter05.html). See `schemas/metadata_schema.json` for the formal JSON schema definition. |
|
|
| All metadata files across all sources share a uniform schema (same keys at every nesting level, same types) to ensure compatibility with Apache Arrow and HuggingFace streaming. |
|
|
| Top-level fields: |
| - `type` |
| - `metadata_information` |
| - `document_description` |
| - `provenance` |
| - `tags` |
| - `schematype` |
| - `additional` - contains source-specific fields (e.g. `additional.unhcr_*` for UNHCR, `additional.wds_*` for WDS API-sourced datasets). |
|
|
| ## Dataset creation |
| The annotations were produced through human labeling using Label Studio. |
|
|
| ## Licensing information |
| MIT License — see [LICENSE](LICENSE) for details. |
|
|
| ## Citation information |
| ```bibtex |
| @misc{dy2026benchmarkingopensourcelayoutdetection, |
| title={Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents}, |
| author={AJ Carl P. Dy and Aivin V. Solatorio}, |
| year={2026}, |
| eprint={2606.06242}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2606.06242}, |
| } |
| ``` |
