| --- |
| license: cc-by-4.0 |
| pretty_name: WB-ChartExtract |
| task_categories: |
| - image-to-text |
| language: |
| - en |
| tags: |
| - chart-extraction |
| - chart-to-table |
| - charts |
| - vision-language |
| - data-extraction |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # WB-ChartExtract |
|
|
| WB-ChartExtract is a benchmark for **chart-to-table extraction**: recovering the underlying |
| numerical table from a chart image. It accompanies the paper *Self-Ensembling |
| Vision-Language Models for Chart Data Extraction*. |
|
|
| It is built from [World Bank Open Data](https://databank.worldbank.org/) time series |
| (52 indicators, 218 countries, 65 years) and is designed to be more challenging than |
| existing benchmarks: charts contain on average **7× more datapoints than ChartQA**, print |
| **no value labels** (removing the OCR shortcut), and span **4 chart types × 4 rendering |
| libraries** with heavy stylistic variation. |
|
|
| - **Code:** https://github.com/tberkane/vlm-ensemble-chart |
|
|
| ## Contents |
|
|
| The dataset contains 1,000 charts: |
|
|
| ``` |
| png/ # 1,000 chart images (1.png ... 1000.png) |
| tables/ # 1,000 ground-truth tables (1.csv ... 1000.csv) |
| metadata.json # per-image metadata |
| ``` |
|
|
| - **png/`<id>`.png** — the chart image. |
| - **tables/`<id>`.csv** — the clean ground-truth table. The first column is the year |
| (index); remaining columns are one series per country. Missing values are `nan`. |
| - **metadata.json** — a dict keyed by image filename with fields: `chart_type` |
| (`line` / `area` / `grouped_bar` / `stacked_bar`), `library` (`matplotlib` / `seaborn` / |
| `plotly` / `bokeh`), `countries`, `series_name`, `num_years`, and `subsampled`. |
|
|
| Chart type and library are assigned uniformly at random (≈62–63 charts per combination). |
| Within each chart, font, color palette, grid, line style, markers, transparency, and figure |
| size are randomized. |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| path = snapshot_download("tberkane/WB-ChartExtract", repo_type="dataset") |
| ``` |
|
|
| ## License |
|
|
| Released under **CC BY 4.0**. Derived from World Bank Open Data, which is also CC BY 4.0. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{berkane2026selfensembling, |
| title = {Self-Ensembling Vision-Language Models for Chart Data Extraction}, |
| author = {Berkane, Thomas and Wang, Qianyi and Majumder, Maimuna S.}, |
| year = {2026} |
| } |
| ``` |
|
|
