---
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}
}
```