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# BBox DocVQA **Train Set**
The BBox DocVQA Train Set is a large-scale dataset designed for training document visual question answering models with grounded supervision. Each QA instance is paired with one or more rendered PDF pages and pixel-level bounding boxes that mark the evidence required to answer the question. The dataset covers a broad distribution of document types, visual regions, and multi-page reasoning patterns.
---
## Repository layout
The dataset is organized as follows:
- **`BBox_DocVQA_Train.jsonl`** – newline-delimited JSON containing all training QA samples and metadata.
- **`<category>/<arxiv-id>/*.png`** – rendered PDF pages grouped into eight arXiv subject categories
(`cs`, `econ`, `eess`, `math`, `physics`, `q-bio`, `q-fin`, `stat`).
- Page images follow the naming format:
**`<arxiv-id>_<page>.png`**, where `<page>` corresponds to the original PDF’s 1-based page index.
This directory layout mirrors the benchmark structure for seamless integration.
---
## Dataset statistics
The BBox DocVQA Train Set contains:
- **Total QA samples:** 30,780
- **Total pages:** 42,380
- **Total papers:** 3,671
### Task type distribution
| Task Type | Count |
|----------|------:|
| SPSBB | 11,668 (37.91%) |
| SPMBB | 7,512 (24.41%) |
| MPMBB | 11,600 (37.69%) |
### Region type distribution
| Region Type | Count |
|-------------|------:|
| Text | 30,424 (60.98%) |
| Image | 12,542 (25.14%) |
| Table | 6,926 (13.88%) |
- **Average bounding box area ratio:** 14.26%
---
## JSON lines schema
Each entry in `BBox_DocVQA_Train.jsonl` follows the schema below:
| Field | Type | Description |
|-------|------|-------------|
| `query` / `question` | string | Natural-language question (duplicate keys for compatibility). |
| `answer` | string | Grounded short-form answer. |
| `category` | string | One of the eight arXiv subject classes. |
| `doc_name` | string | ArXiv identifier of the source paper. |
| `evidence_page` | list[int] | Pages containing the evidence (1-based). |
| `image_paths` / `images` | list[str] | Relative paths to one or two rendered PDF pages. |
| `bbox` | list[list[list[int]]] | Bounding boxes for each referenced page, in pixel units. |
| `subimg_tpye` | list[list[str]] | Region type per bounding box (`text`, `table`, or `image`). |
---
## Example
```json
{
"query": "What is the caption of Figure 3 on the referenced page?",
"answer": "Comparison between the baseline and our method",
"doc_name": "2301.12345",
"category": "cs",
"evidence_page": [4],
"image_paths": ["cs/2301.12345/2301.12345_4.png"],
"bbox": [
[[512, 1340, 1880, 1620]]
],
"subimg_tpye": [["image"]]
}
```
---
## Quick start
```python
import json
from PIL import Image, ImageDraw
with open("BBox_DocVQA_Train.jsonl") as f:
sample = json.loads(f.readline())
for page_path, boxes in zip(sample["image_paths"], sample["bbox"]):
img = Image.open(page_path).convert("RGB")
draw = ImageDraw.Draw(img)
for (xmin, ymin, xmax, ymax) in boxes:
draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=5)
img.show()
```
---
## Notes and usage guidance
- Page images are uncompressed PNG renders produced from arXiv PDFs; please observe arXiv’s terms of use for any redistribution.
- Bounding boxes are provided in absolute pixel coordinates; normalize them by image width/height when required.
- Duplicate key names (e.g., `query`/`question`, `image_paths`/`images`) are intentionally preserved for compatibility.
- The train set provides large-scale grounded supervision across diverse document layouts and visual evidence types.
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