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filename
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7
11
image
unknown
annotation
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2.07k
5.78k
chart_type
stringclasses
1 value
title
stringlengths
0
15
num_features
int32
1
3
num_categories
int32
2
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value_min
float32
10
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value_max
float32
50
210
train_0
[ 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 4, 248, 0, 0, 3, 186, 8, 6, 0, 0, 0, 2, 112, 233, 115, 0, 0, 0, 58, 116, 69, 88, 116, 83, 111, 102, 116, 119, 97, 114, 101, 0, 77, 97, 116, 11...
{ "chart_metadata": { "title": { "text": "vdpWX", "bbox": [608,23,733,58] }, "type": "vertical", "bar_width": 100, "legend": { "entries": [], "bbox": [0,0,0,0] }, "size": { "bbox": [0,0,1272,954] }, "origin": { "bbox": [89,881,97,889] } }, ...
vertical
vdpWX
3
3
10
115
train_1
[ 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 4, 72, 0, 0, 3, 191, 8, 6, 0, 0, 0, 220, 75, 243, 249, 0, 0, 0, 58, 116, 69, 88, 116, 83, 111, 102, 116, 119, 97, 114, 101, 0, 77, 97, 116, 11...
{ "chart_metadata": { "title": { "text": "cVNvZMf", "bbox": [114,20,242,49] }, "type": "vertical", "bar_width": 37, "legend": { "entries": [ { "feature": { "text": "bHiA", "bbox": [955,428,1025,457] }, "patch": { ...
vertical
cVNvZMf
2
4
10
143
train_2
[ 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 4, 110, 0, 0, 5, 43, 8, 6, 0, 0, 0, 123, 47, 225, 1, 0, 0, 0, 58, 116, 69, 88, 116, 83, 111, 102, 116, 119, 97, 114, 101, 0, 77, 97, 116, 112,...
{ "chart_metadata": { "title": { "text": "U cHT", "bbox": [972,27,1106,73] }, "type": "vertical", "bar_width": 84, "legend": { "entries": [], "bbox": [0,0,0,0] }, "size": { "bbox": [0,0,1134,1323] }, "origin": { "bbox": [129,1215,138,1224] } ...
vertical
U cHT
1
3
10
179
train_3
[ 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 3, 219, 0, 0, 3, 78, 8, 6, 0, 0, 0, 1, 34, 31, 44, 0, 0, 0, 58, 116, 69, 88, 116, 83, 111, 102, 116, 119, 97, 114, 101, 0, 77, 97, 116, 112, ...
{ "chart_metadata": { "title": { "text": "", "bbox": [0,0,0,0] }, "type": "vertical", "bar_width": 28, "legend": { "entries": [ { "feature": { "text": "FKkjW", "bbox": [338,758,412,785] }, "patch": { "bbox"...
vertical
3
5
10
142
train_4
"iVBORw0KGgoAAAANSUhEUgAABIwAAAVOCAYAAAAQAaKkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"yukgv okj KIn\",\n \"bbox\": [(...TRUNCATED)
vertical
yukgv okj KIn
3
4
10
161
train_5
"iVBORw0KGgoAAAANSUhEUgAAAmQAAAMwCAYAAACOeXzpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"uriYvOoq\",\n \"bbox\": [257,1(...TRUNCATED)
vertical
uriYvOoq
1
2
10
117
train_6
"iVBORw0KGgoAAAANSUhEUgAAA5gAAAMlCAYAAAAWnFwWAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"\",\n \"bbox\": [0,0,0,0]\n (...TRUNCATED)
vertical
1
5
10
184
train_7
"iVBORw0KGgoAAAANSUhEUgAAA2QAAAPgCAYAAABdyb8GAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"\",\n \"bbox\": [0,0,0,0]\n (...TRUNCATED)
vertical
3
3
10
188
train_8
"iVBORw0KGgoAAAANSUhEUgAABhgAAAYYCAYAAABrEsKBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"R pSN\",\n \"bbox\": [758,27,8(...TRUNCATED)
vertical
R pSN
3
4
10
142
train_9
"iVBORw0KGgoAAAANSUhEUgAABNAAAATQCAYAAAAyMrjlAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED)
"{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"DLHjJwjNqn\",\n \"bbox\": [135(...TRUNCATED)
vertical
DLHjJwjNqn
3
4
10
171
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Bar Chart Annotation Dataset

A collection of synthetic bar chart images with detailed structural annotations. Split into three subsets for different stages of a chart understanding pipeline.


Subsets

Subset Folder Train Test
encoder_training hf_ready/encoder_training/ 100,000 100
decoder_training hf_ready/decoder_training/ 17,000 3,400
pipeline_testing hf_ready/pipeline_testing/ 100

Each subset is a standalone HuggingFace dataset (its own repo), formatted as sharded Parquet files under a data/ subfolder.


Columns

Column Type Description
filename string Original file stem (e.g. train_42) — use to reconstruct or cross-reference source files
image binary Original PNG bytes (RGBA, 1272×954)
annotation string Verbatim JSON annotation — identical to the source .json files
chart_type string "vertical" or "horizontal"
title string Chart title text
num_features int32 Number of data features (legend entries)
num_categories int32 Number of category-axis ticks
value_min float32 Value axis minimum
value_max float32 Value axis maximum

Loading

from datasets import load_dataset

ds = load_dataset("<org>/encoder_training")
sample = ds["train"][0]

# Image
from PIL import Image
from io import BytesIO
img = Image.open(BytesIO(sample["image"]))

# Annotation (full structure, unchanged)
import json
ann = json.loads(sample["annotation"])
# ann["chart_metadata"] — title, type, bar_width, legend, origin, size
# ann["data"]           — category_axis, value_axis, features

Annotation Format

Each annotation follows this schema (see format.json for the full JSON Schema):

{
  "chart_metadata": {
    "title":     { "text": "...", "bbox": [x1, y1, x2, y2] },
    "type":      "vertical" | "horizontal",
    "bar_width": <int>,
    "legend": {
      "bbox": [...],
      "entries": [
        { "feature": { "text": "...", "bbox": [...] },
          "patch":   { "bbox": [...] } }
      ]
    },
    "origin": { "bbox": [...] },
    "size":   { "bbox": [...] }
  },
  "data": {
    "category_axis": {
      "label": { "text": "...", "bbox": [...] },
      "bbox":  [...],
      "ticks": [ { "label": { "text": "...", "bbox": [...] }, "bbox": [...] } ]
    },
    "value_axis": {
      "label":     { "text": "...", "bbox": [...] },
      "min_value": <number>,
      "max_value": <number>,
      "bbox":      [...],
      "ticks":     [ { "label": { "text": "...", "bbox": [...] }, "bbox": [...] } ]
    },
    "features": [
      {
        "feature": "...",
        "data": [
          { "category": "...", "value": <number>, "bbox": [...] }
        ]
      }
    ]
  }
}

All bounding boxes are [x1, y1, x2, y2] in pixel coordinates.

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Models trained or fine-tuned on dralois/Bar-JEPA