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. 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 - 🧪 Try it:
nutrientdocs/chart-parsing-demo - 🏆 Leaderboard:
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
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
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, 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.
About the author
This project is maintained and funded by Nutrient - 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.