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BBox_DocVQA_Train.jsonl

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BBox_DocVQA_Train.tar.gz

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README.md

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----
-license: apache-2.0
----
+# 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.
+