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license: cc-by-4.0
pretty_name: Chart Parsing Benchmark
language:
- en
task_categories:
- image-to-text
- image-text-to-text
tags:
- chart
- chart-understanding
- chart-to-table
- structured-extraction
- benchmark
- leaderboard
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Chart Parsing Benchmark
**Chart image in, structured JSON out.** A fixed, test-only benchmark for turning a chart image into its
title, chart type, data table, encoding, axes, series, legend, and data labels. It holds
**2500 charts** and is the set behind the [leaderboard](https://huggingface.co/spaces/nutrientdocs/chart-parsing-leaderboard). Every entry sees the same charts and the same
prompt, and the reference scorer ships in this repository, so any result here can be reproduced.
- 🎯 **Model:** [`nutrientdocs/chart-parsing-vlm`](https://huggingface.co/nutrientdocs/chart-parsing-vlm)
- 🧪 **Try it:** [`nutrientdocs/chart-parsing-demo`](https://huggingface.co/spaces/nutrientdocs/chart-parsing-demo)
- 🏆 **Leaderboard:** [`nutrientdocs/chart-parsing-leaderboard`](https://huggingface.co/spaces/nutrientdocs/chart-parsing-leaderboard)
| chart type | charts |
| --- | --- |
| line | 552 |
| bar | 463 |
| pie | 249 |
| scatter | 235 |
| area | 224 |
| box | 171 |
| bubble | 164 |
| heatmap | 100 |
| histogram | 54 |
| treemap | 54 |
| combo | 51 |
| funnel | 49 |
| radar | 46 |
| violin | 33 |
| stem | 25 |
| step | 15 |
| candlestick | 15 |
## Task and prompt
Every entry on the leaderboard uses the same system prompt and instruction, shipped with the scorer. The
model returns one JSON object. The `target` column holds the reference JSON as a string.
## Metrics
The ranking metric is the **mean per-chart cell F1 at 3% tolerance** (`mean_f1_tol3`): each chart's data cells
are scored for precision, recall, and F1, and the per-chart F1 is averaged so every chart counts once,
whatever its table size. Cells are never pooled across charts.
A cell matches when its row identity (the first column plus every text column) and its column agree and
the value is close enough. Row order never matters. Tables with a single value column, or whose rows all
repeat one value, match on row identity alone. Three tolerance levels are reported side by side:
| metric | a value counts as correct when it is |
|---|---|
| `mean_f1` | strict: relative 1e-3 |
| `mean_f1_tol3` | exact (relative 1e-3) when the chart prints data labels, 3% in percentage points for share charts (pie, donut, 100%-stacked, treemap), otherwise 3% of the chart's value range. Histogram bin-edge columns are not scored; histograms are scored on the per-bin frequency |
| `mean_f1_tol5` | exact (relative 1e-3) when the chart prints data labels, 5% in percentage points for share charts (pie, donut, 100%-stacked, treemap), otherwise 5% of the chart's value range. Histogram bin-edge columns are not scored; histograms are scored on the per-bin frequency |
The 3% level is the ranking metric because it matches what a careful reader can recover from the pixels;
the strict level exposes models that copy printed labels well, and the 5% level shows how much is lost
to small estimation error. Also reported: table shape match, per-field accuracy (chart type, title, axis
labels, scales, legend, data labels, column types and roles), and set F1 for `encoding` and `series`.
## Scoring
```bash
python score.py --benchmark-repo nutrientdocs/chart-parsing-benchmark \
--predictions preds.jsonl --out result.json --name my-model
```
`preds.jsonl` has one `{"id": ..., "text": ...}` line per chart, where `text` is the raw model output.
The scorer is self-contained and needs only `datasets`. Submit the resulting JSON with the model ID,
license, and runtime details for review. Entries on the leaderboard carry one of three labels:
| label | meaning |
|---|---|
| `commercial` | Nutrient model, weights under a commercial Nutrient license |
| `provider` | cloud model reached through its vendor API |
| `open` | open-weight model served locally with the benchmark prompt |
## Schema
```python
from datasets import load_dataset
benchmark = load_dataset("nutrientdocs/chart-parsing-benchmark", split="test")
```
| Field | Meaning |
|---|---|
| `id` | Stable chart identifier |
| `image` | RGB chart image |
| `chart_type`, `title` | Flat copies of target fields for filtering |
| `category` | Source label from ChartGen |
| `data_source` | Always `render` in this set |
| `target` | Reference JSON as a string |
## License and attribution
Charts derive from [SD122025/ChartGen-200K](https://huggingface.co/datasets/SD122025/ChartGen-200K),
released under CC BY 4.0. This benchmark keeps the same license. The Nutrient Chart Parsing model
evaluated on it is commercial; to evaluate or deploy it, [contact Nutrient](https://www.nutrient.io/contact-sales/).
## About the author
<a href="https://nutrient.io/">
<img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
</a>
This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.
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