2077AIDataFoundation/DrDocBench

Dr.DocBench

A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

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Public subset of the full 4,514-page benchmark.

Dataset Summary

Dr.DocBench is a difficulty-aware benchmark for evaluating whether vision-language models and document processing systems can parse expert-level, structurally complex documents.

This release contains 986 annotated pages of English-language documents from 66 long-form books, covering 38 BISAC subject domains, with 12,902 block-level annotations. It is a public subset of the full benchmark, which spans 312 PDFs, 4,514 pages, 52 subject domains, and ~70k annotations.

Dr.DocBench targets three gaps in current OCR and document parsing evaluation: saturation on common genres, coarse page-level granularity, and missing expert-domain structures such as chemical formulas, music notation, complex tables, and cross-page layouts.

Instead of sampling pages uniformly, Dr.DocBench selects documents through parser-failure-based sampling: candidate books are scored by inter-parser disagreement across three state-of-the-art systems, and only high-disagreement documents enter annotation. Each source document runs ~100 pages, from which a consecutive ~10–15-page run is annotated for layout, reading order, hierarchical relations, and domain-specific visual content.

In the current evaluation, 12 pipeline parsers and frontier VLMs were tested. The best overall score is 61.94, and REFERENCE appears in the worst-5 subjects for every evaluated system.

Note. Of the 986 pages, 970 carry annotations and 16 are blank pages stored as empty records. Statistics below describe this subset; Evaluation Results reports scores on the full benchmark.

Task Format

The task is to convert one or more consecutive document page images into a single continuous Markdown document that preserves both content and structure.

Each evaluation instance consists of:

• input: n consecutive page images (n = 2 by default; n = 1 for Music)
• output: one Markdown document covering all n pages in reading order
• reference: the human-verified layout_dets blocks for those pages

A unified inference prompt is used for every model, with no model-specific prompt engineering. It fixes the output convention for each content type:

Content	Required output format
Text	Original language and script, no translation, no guessing on illegible scans
Multi-column layout	Columns emitted in natural reading order (left-to-right, top-to-bottom)
Formulas	LaTeX — \( ... \) inline, \[ ... \] display
Chemistry	\ce{} (mhchem) for reactions; SMILES verbatim in a ```smiles block
Tables	HTML wrapped in <table>, using rowspan / colspan; multi-page tables merged into one table
Code and pseudocode	Fenced code block with a language tag
Music scores	MusicXML in a ```musicxml block, no <?xml?> declaration or <score-partwise> wrapper
Figures	Purely visual figures ignored; embedded labels and axis text transcribed as plain text

The evaluation script aligns the predicted Markdown against the ground-truth blocks and scores each component separately (text, formula, table, reading order) before combining them into the overall score. The full prompt is in Appendix G (Figures 9–10) of the paper.

Supported Tasks

Task	Annotation used	Metric
Text recognition	text on textual blocks	Normalized Levenshtein edit distance ↓
Layout analysis	category_type + poly	Detection over 20+ block categories
Reading-order prediction	order	Reading-order edit distance ↓
Table recognition	html / latex on table blocks	TEDS / TEDS-S ↑
Formula recognition	latex on equation_isolated	CDM ↑ / edit distance ↓
Structural relations	extra.relation	Caption linking, cross-page continuation
Optical Music Recognition	MusicXML transcription	Edit distance ↓ / note-level F1 ↑

The overall score maps each component to a 0–100 scale — text and reading order are (1 − edit distance) × 100, formulas are CDM × 100, tables are TEDS × 100 — and averages the components available per sample, excluding missing elements from the denominator.

Dataset Structure

Each example is one annotated page, stored as <BISAC_SUBJECT>/<document_uuid>/json/<document_uuid>_page_<N>.json, alongside its source image (images/page_<N>.jpg) and a rendered Markdown version (mds/<document_uuid>_<N>.md). <N> is the original page number in the source book, so a document's annotated run is a consecutive slice such as page_2152 … page_2166. The JSON is the ground truth; the Markdown is rendered from it for convenience.

Each example typically contains the following components:

Field	Type	Description
page_info.page_no	integer	Page number in the source book.
page_info.height / width	integer	Page image dimensions in pixels.
page_info.image_path	string	Relative path to the page image.
page_info.page_attribute	object	Page-level metadata: subject (one of 52 BISAC domains), challenge_type (perception / structural_reconstruction / domain_reasoning), data_source (one of nine genres), language (a string or a list of strings), layout (single_column / double_column / three_column / 1andmore_column / other_layout), and special_issue (difficulty flags such as fuzzy_scan, table_wireless_line).
layout_dets	array	Block annotations, one object per region.
layout_dets[].category_type	string	Block category — one of 20 types including text_block, title, figure, table, equation_isolated, code_algorithm, header, page_number.
layout_dets[].poly	array	Quadrilateral as [x1,y1, x2,y2, x3,y3, x4,y4] in page pixels.
layout_dets[].order	integer	Reading-order index. Absent on page elements (header, footer, page number) and masks.
layout_dets[].anno_id	integer	Page-unique block id, referenced by relations.
layout_dets[].text	string	Transcribed content, Markdown-flavored.
layout_dets[].latex	string	LaTeX for equations, and a LaTeX rendering for tables.
layout_dets[].html	string	HTML structure with rowspan / colspan for tables.
layout_dets[].attribute	object	Category-dependent attributes: text rotation and background, table line style and span, formula type.
extra.relation	array	Inter-block links as {source_anno_id, target_anno_id, relation_type} — parent_son connects a figure/table/equation to its caption or footnote; truncated marks two blocks forming one logical unit, with target_anno_id: -1 for cross-page continuation.

Example Instance

A real record — EDUCATION/a8727f40-.../json/a8727f40-..._page_258.json — showing a borderless horizontal table with a rotated caption, a footnote, body text, and a page number. Long text, latex, and html values are elided with ...:

[{
  "page_info": {
    "page_name": "a8727f40-f59f-4e0d-be5b-4104252375b2",
    "page_no": 258,
    "height": 744,
    "width": 462,
    "image_path": "../images/a8727f40-f59f-4e0d-be5b-4104252375b2/page_258.jpg",
    "original_image_url": "https://.../EDUCATION/a8727f40-.../page_258.jpg",
    "page_attribute": {
      "data_source": "academic_literature",
      "subject": "EDUCATION",
      "challenge_type": "structural_reconstruction",
      "language": "english",
      "layout": "other_layout",
      "special_issue": ["table_horizontal", "table_fewer_line"]
    }
  },
  "layout_dets": [
    {
      "category_type": "table_caption",
      "poly": [42.92, 348.19, 54.12, 348.19, 54.12, 673.85, 42.92, 673.85],
      "ignore": false,
      "order": 1,
      "anno_id": 9,
      "text": "**Table 20.1.** Tabulated Results from Student Reflection Essays",
      "attribute": {
        "text_language": "text_english",
        "text_background": "single_colored",
        "text_rotate": "rotate270"
      }
    },
    {
      "category_type": "table",
      "poly": [56.43, 48.95, 380.21, 48.95, 380.21, 673.39, 56.43, 673.39],
      "ignore": false,
      "order": 2,
      "anno_id": 2,
      "latex": "\\begin{table}[]\n\\begin{tabular}{llll}\n\\textbf{Important Aspects...}",
      "html": "<table>\n<tbody>\n<tr>\n<td style=\"text-align: left;\"><strong>Important Aspects...",
      "attribute": {
        "table_layout": "vertical",
        "line": "fewer_line",
        "language": "table_en",
        "with_span": "false",
        "include_background": "false",
        "include_equation": "false",
        "include_photo": "false",
        "with_structured_text": "true"
      }
    },
    { "category_type": "table_footnote", "order": 3, "anno_id": 7, "text": "...", "...": "..." },
    { "category_type": "text_block",     "order": 4, "anno_id": 4, "text": "...", "...": "..." },
    { "category_type": "page_number",                "anno_id": 12, "text": "258", "...": "..." }
  ],
  "extra": {
    "relation": [
      { "source_anno_id": 2, "target_anno_id": 7, "relation_type": "parent_son" },
      { "source_anno_id": 2, "target_anno_id": 9, "relation_type": "parent_son" }
    ]
  }
}]

Three things to note in this record:

• order skips the page number. Blocks 1–4 carry reading order; page_number has no order key, because page elements are excluded from reading-order evaluation.
• Relations point from parent to child. Both relations have the table (anno_id: 2) as source, linking it to its footnote (7) and caption (9).
• Attributes encode the difficulty. line: fewer_line plus text_rotate: rotate270 is exactly the borderless-table-with-rotated-caption case that causes large TEDS drops — see Evaluation Results.

Loading

import glob, json

pages = []
for path in sorted(glob.glob("*/*/json/*.json")):
    record = json.load(open(path))
    if record:
        pages.append(record[0])

page = pages[0]
blocks = [b for b in page["layout_dets"] if "order" in b and not b["ignore"]]
for b in sorted(blocks, key=lambda b: b["order"]):
    print(b["order"], b["category_type"], (b.get("text") or "")[:60])

Blank pages are stored as empty lists, so if record skips them. order is absent on page elements such as headers, footers, and page numbers, which are excluded from reading-order evaluation.

Dataset Statistics

All figures below describe this public subset. Each document contributes a consecutive run of up to 15 pages; page counts total 970, excluding 16 blank pages.

Data source	Pages	Layout	Pages	Challenge type	Pages
book	726	single_column	494	structural_reconstruction	580
magazine	84	other_layout	193	perception	387
academic_literature	75	double_column	146	domain_reasoning	2
colorful_textbook	70	1andmore_column	95
exam_paper	14	three_column	42
note	1

Some cells are too thin to analyze on their own: note (1 page), domain_reasoning (2 pages), and exam_paper (14 pages) are present for schema completeness rather than as evaluable slices. Breakdowns along these axes should be read from the full benchmark, not this subset.

Subjects (documents per subject): EDUCATION 5 · HOUSE&HOME 4 · DESIGN, GAMES&ACTIVITIES, JUVENILENONFICTION, SOCIALSCIENCE, SPORTS&RECREATION, YOUNGADULTFICTION 3 each · BUSINESS&ECONOMICS, COMPUTERS, CRAFTS&HOBBIES, GARDENING, PHILOSOPHY, POETRY, POLITICALSCIENCE, STUDYAIDS, TRANSPORTATION 2 each · ARCHITECTURE, ART, BIOGRAPHY&AUTOBIOGRAPHY, BODY,MIND&SPIRIT, COMICS&GRAPHICNOVELS, COOKING, FAMILY&RELATIONSHIPS, FICTION, HISTORY, HUMOR, LANGUAGEARTS&DISCIPLINES, LAW, LITERARYCRITICISM, MEDICAL, PERFORMINGARTS, PETS, PSYCHOLOGY, SELF-HELP, TECHNOLOGY&ENGINEERING, TRUECRIME, YOUNGADULTNONFICTION 1 each.

Difficulty flags: colorful_backgroud 157 · fuzzy_scan 140 · table_full_line 40 · table_fewer_line 25 · table_horizontal 18 · table_wireless_line 15 · table_span 2 · table_with_formula 2.

Language: English.

Evaluation Results

For the full 4,514-page benchmark as reported in the paper. Bold = best.

Model	Size†	Access	Text↓	Form.↓	CDM↑	TEDS↑	TEDS-S↑	Order↓	Overall↑
MinerU 2.5	1.2B	Open	0.33	0.51	24.15	55.85	63.70	0.30	54.37
PaddleOCR	0.9B	Open	0.73	0.42	30.73	51.79	59.94	0.71	34.78
GPT-5.5	–	Closed	0.19	0.36	34.55	48.90	58.96	0.17	61.94
Kimi-K2.5	32B	Open	0.19	0.35	27.04	51.98	61.09	0.18	60.38
Claude Opus 4.6	–	Closed	0.22	0.37	32.02	49.21	58.12	0.19	60.19
Gemini 3.1 Pro	–	Closed	0.22	0.35	32.22	51.26	59.03	0.21	60.13
Qwen3.5-Flash	3B	Open	0.26	0.32	35.69	46.24	54.38	0.26	57.51
Qwen3.5-Plus	17B	Open	0.25	0.31	30.40	49.77	58.23	0.26	57.32
Qwen3.5-122B-A10B	10B	Open	0.23	0.32	32.54	38.31	44.99	0.23	56.32
Doubao-Seed-1.6-Vision	–	Closed	0.34	0.31	41.28	33.93	42.16	0.28	53.14
GPT-4o	–	Closed	0.37	0.47	36.02	30.14	38.62	0.31	49.73
Nemotron-Nano-12B	12B	Open	0.62	0.76	12.82	27.03	34.09	0.564	30.33

†activated parameters. A unified inference prompt is used for all models, with a default 2-page sliding window (1 page for Music).

Limitations

• Scope. A diagnostic benchmark for parsing and structural recognition, not for downstream document-intelligence tasks such as QA or retrieval.
• Standardized prompting. One unified prompt is used for all models, so results reflect standardized rather than best-achievable performance. Prompt-incapable parsers (MinerU, PaddleOCR) cannot follow format instructions at all, and their scores on prompt-dependent outputs are reference baselines only.
• Subject is one axis of difficulty. Layout density, scan quality, multilingual content, and cross-page continuation also contribute, so subject-level analysis complements block- and attribute-level analysis.
• Format-based metrics. LaTeX, HTML, and MusicXML enable scalable automatic evaluation but do not capture equivalent representations; semantic or execution-based metrics would improve this.
• Music is exploratory — a 6-document probe, scored by string-level edit distance that conflates schema verbosity with musical content.

Ethical Considerations

Dr.DocBench is intended for research evaluation of document parsing systems. During construction and QC, pages containing clearly sensitive personal information, private identifying information, or inappropriate content were removed when identified, and annotators were instructed to flag such cases for review.

The annotations are released under CC0 1.0 Universal (public domain dedication). The underlying source documents retain the rights of their original publishers and may remain subject to their original access, copyright, and licensing conditions — source images come from publicly available books and scanned documents, used solely for benchmark construction and research evaluation under fair-use principles. Users should follow the terms of the original sources.

Citation

Citation metadata will be updated when the paper is public.

@misc{drdocbench2026,
  title        = {Dr.DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing},
  author       = {Dr.DocBench Team},
  year         = {2026},
  howpublished = {\url{https://github.com/2077AI/DrDocBench}},
  note         = {Dataset and benchmark}
}