Datasets:

Theseus-Lab
/

AmbiHTMLBench

pretty_name: AmbiHTMLBench
license: mit
language:
  - en
  - zh
task_categories:
  - image-to-text
tags:
  - document-understanding
  - html
  - benchmark
size_categories:
  - 1K<n<10K
configs:
  - config_name: pages
    data_files:
      - split: test
        path: index/pages.jsonl

AmbiHTMLBench

Reconstruct document page images as executable HTML.

Code | Browse pages | Download ZIP

AmbiHTMLBench evaluates content, structure, and visual fidelity in image-to-HTML reconstruction. The benchmark covers institutional PDF documents, web-native documentation, newspapers and magazines, and posters or design-heavy pages.

What is included

Material	This release
Reference page images	1,115
Annotated blocks	28,939
Annotation version	GT v5
Evaluation split	`test`

Each page has an original image and a complete block-annotation JSON file. Images retain their original bytes, resolution and format. The test split is for evaluation; it is not a training split. This package contains reference materials, not model predictions or judge records.

Download and use

Download the complete ZIP and extract it, or fetch the repository:

from huggingface_hub import snapshot_download

snapshot_download(repo_id="Theseus-Lab/AmbiHTMLBench", repo_type="dataset",
                  local_dir="AmbiHTMLBench", ignore_patterns=["archives/*"])

Read a page from the downloaded directory:

import json
from pathlib import Path
from PIL import Image

root = Path("AmbiHTMLBench")
rows = [json.loads(line) for line in (root / "index/pages.jsonl").read_text(encoding="utf-8").splitlines()]
page_id = "0001"
page = next(row for row in rows if row["page_id"] == page_id)
image = Image.open(root / page["image_path"])
annotation = json.loads((root / page["annotation_path"]).read_text(encoding="utf-8"))
assert annotation["ID"] == int(page_id)

For the lightweight index only:

from datasets import load_dataset

pages = load_dataset("Theseus-Lab/AmbiHTMLBench", "pages", split="test")

The viewer contains one metadata row per page. Full images and annotations are in the linked page directories; loading the index does not download them. Use a fixed Hub commit revision when reproducing an experiment.

Dataset structure

README.md
LICENSE
release.json
manifest.json
SHA256SUMS
index/pages.jsonl
pages/0001/image.<original-format>
pages/0001/annotation.json
...
annotations/AmbiHTMLBench_GT_v5_1115.json
archives/AmbiHTMLBench.zip

Index rows contain page_id, path, image_path, annotation_path, image_width, image_height, block_count, and annotation_version. All file paths are relative to the dataset repository root.

Page IDs are four-digit strings (0001 through 1115 in the full release). Each ID links the image and ground truth in pages/<page_id>/; the index provides their exact local paths. The numeric ID in each annotation and in the aggregate ground truth identifies the same page.

Each annotation.json contains ID and the complete original contentList. Blocks contain semantic labels in name, text/table/formula content in remark, and pixel coordinates in left, top, width, height where present. Optional block fields and Unicode text are retained. The aggregate GT file contains the same page annotations. Source image URL fields are omitted from the index, annotations, manifest and ZIP. Images are loaded from downloaded files using the ID-to-path mapping; no source URL lookup is needed or provided.

SHA256SUMS covers expanded files and the ZIP, excluding itself. manifest.json records source and image identities and expanded-file digests, excluding itself and the ZIP. The ZIP contains the expanded release and manifest, without the outer ZIP or SHA256SUMS.

Evaluation

Install and follow the evaluation code. Use the aggregate GT JSON and separately generated HTML predictions to measure table, text and layout fidelity. For offline reference visualizations, resolve each page's local image through the index. This GT v5 release is separate from the frozen OLS5 subjective experiment's input snapshot.

Materials and intended use

Reference pages include diverse layouts and multilingual text. Images are included in the release and can be used offline after download. The public annotations are evaluation references and should not be supplied as inputs to models being evaluated. Exposure or training contamination can affect comparisons.

License

This dataset is released under the MIT License.