README.md

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.

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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.

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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%

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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).

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Example

{
  "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"]]
}

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Quick start

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()

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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.