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Omni Parse Bench

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Co-authored-by: hheiden <hheiden@users.noreply.huggingface.co>
Co-authored-by: vikp <vikp@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ pretty_name: Omni Parse Bench
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+ license: other
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+ license_name: per-source
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+ license_link: https://huggingface.co/datasets/datalab-to/omni_parse_bench/blob/main/licenses/README.md
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+ task_categories:
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+ - image-to-text
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+ tags:
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+ - document-parsing
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+ - ocr
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+ - benchmark
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+ size_categories:
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+ - 1K<n<10K
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: test
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+ path: manifest.parquet
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+ ---
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+
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+ # Omni Parse Bench
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+
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+ Choosing a document parser usually starts with a leaderboard: one number per parser. But nobody parses an
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+ average document, and the parser that wins on average can be the one that fails on yours. Omni Parse Bench
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+ is built to answer the question you actually have: which parser is best on documents like mine? It is:
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+
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+ - simple to reason about: every test is a yes/no question about one page (is this content in the output, in
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+ this order, in a table cell under these headings, in a layout block of this kind?), and every score is the
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+ share of tests passed;
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+ - explorable: every test is tagged by what its content is (a table cell, handwriting, a rotated page, Thai),
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+ so any slice of the benchmark gets its own score; and
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+ - clear about its gaps: every score comes with the number of tests and documents behind it, so a thin slice
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+ says it is thin.
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+
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+ It has 16,288 tests on 2,937 pages from 2,343 documents over 22 suites, in
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+ 92 languages. Omni Parse Bench is developed by Datalab.
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+
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+ Read more in our [blog post](https://www.datalab.to/blog/omni-parse-bench). Also see the [GitHub](https://github.com/datalab-to/omni_parse_bench) for the scorer and the harness that
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+ runs parsers on the benchmark.
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+
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+ ## The design
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+
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+ ![The benchmark's design: headline, families, tests, args and tags](./assets/model.svg)
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+
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+ The headline is the mean of three family scores: text, tables and layout. A family is a group of test
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+ types:
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+
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+ | test type | checks | family | tests |
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+ |---|---|---|---:|
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+ | `present` | this text, number or equation appears | text | 7,275 |
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+ | `order` | text A comes before text B | text | 1,189 |
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+ | `repeat` | this line appears exactly N times | text | 35 |
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+ | `table_cell` | this cell sits under these headings and beside these neighbors | tables | 5,386 |
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+ | `layout_kind` | the block holding this line is a heading, text or table | layout | 2,403 |
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+
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+ A test checks one or more args, each of which is the content at one place on the page. Each arg is
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+ tagged:
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+
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+ - structure: `table`, `math`, `form` and `multi_column`;
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+ - rendering: `handwriting`, `tiny`, `rotated` and `degraded`;
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+ - role: `heading`, `caption`, `footnote`, `list`, `code`, `figure` and `header_footer`;
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+ - script: ISO 15924 codes (`Latn`, `Hani`, `Deva`, ...); and
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+ - language: ISO 639-3 codes (`eng`, `fra`, `tha`, ...).
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+
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+ A test's tags are the union of its args' tags. Tags never decide whether a test passes or what the
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+ headline is; they select tests, so any slice of the benchmark can be scored. A test with several tags
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+ counts in each, so per-tag scores overlap and don't average to the headline.
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+
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+ Each tag was decided from the page alone: computed where it can be (a script from Unicode, a font
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+ size, a rotation we made), from a source where one exists (publisher markup, form widgets),
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+ else by two models shown the content boxed on the page, with a third settling their splits by fixed
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+ rules; whatever they couldn't settle was decided by a person, who also checked a random sample of 30
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+ tagged pieces of content and found no wrong tag.
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+
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+ ## Data
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+
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+ ![Tests, pages and documents per tag](./assets/tag-coverage.svg)
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+
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+ ![Documents with a test carrying both tags, for every pair of tags](./assets/tag-pairs.svg)
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+
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+ ![Every language, by its documents and tests](./assets/languages.svg)
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+
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+ ## Results
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+
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+ | parser | headline | text | tables | layout |
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+ | --- | ---: | ---: | ---: | ---: |
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+ | Gemini 3.8 Flash* | 92.9 | 91.3 | 95.2 | 92.2 |
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+ | Datalab accurate | 92.7 | 90.8 | 91.8 | 95.6 |
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+ | Datalab balanced | 91.6 | 90.0 | 89.2 | 95.5 |
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+ | Reducto | 90.8 | 85.4 | 91.2 | 95.8 |
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+ | LlamaParse | 90.4 | 88.2 | 92.6 | 90.4 |
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+ | Mistral OCR | 89.5 | 82.8 | 91.9 | 93.8 |
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+ | Claude Sonnet 5.5* | 88.7 | 93.1 | 95.4 | 77.5 |
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+ | GPT-5.6 Sol | 87.8 | 85.4 | 91.6 | 86.5 |
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+ | Extend | 83.3 | 64.1 | 92.5 | 93.4 |
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+ | Azure | 83.0 | 74.6 | 81.7 | 92.8 |
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+ | Tesseract | none | 17.6 | 0.0 | unsupported |
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+
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+ \* Used in creating and verifying at least some tests, so not ranked.
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+
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+ The headline is the mean of three family scores: text (`present`, `order` and `repeat` tests), tables
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+ (`table_cell`) and layout (`layout_kind`). Tesseract returns no layout, so it has no headline.
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+
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+ ![Headline against median seconds per page](./assets/latency.svg)
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+
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+ ![Headline against price per 1,000 pages](./assets/cost.svg)
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+
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+ ![Each parser's pass rate on a tag's tests, minus their median](./assets/weak-spots.svg)
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+
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+ ## Layout
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+
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+ ```
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+ manifest.parquet one row per page
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+ pages/<page_id>.pdf the page that gets sent: a one-page PDF, or a .png for an image page
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+ gold/<page_id>.json its tests: {"tests": [...]}
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+ licenses/ the license, and the credit it requires, of each source
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+ assets/ the charts on this card
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+ ```
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+
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+ | column | |
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+ |---|---|
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+ | `page_id` | unique; the files carry it |
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+ | `document` | the document the page comes from; scores count documents as well as tests |
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+ | `suite` | which part of the benchmark the page came from |
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+ | `doc_path` | `pages/<page_id>.pdf` (or `.png`), relative to the dataset root |
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+ | `gt_path` | `gold/<page_id>.json`, relative to the dataset root |
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+
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+ A test says what structure of the output it checks (`test_type`), in which output (`output_type`),
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+ on what content (`args`), with what else it needs (`settings`); how its answer was established
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+ (`derivation`); and what its content is (`tags`):
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+
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+ ```json
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+ {
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+ "id": "p11_PMC12397559.1_t0_head_17_2",
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+ "test_type": "table_cell",
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+ "output_type": "html",
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+ "derivation": "publisher_markup",
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+ "tags": ["Latn", "eng", "multi_column", "table"],
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+ "args": {
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+ "cell": {"text": "32 (40.0)", "max_diffs": 0},
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+ "headings": [{"text": "No. (%)", "max_diffs": 0}]
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+ },
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+ "settings": {}
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+ }
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+ ```
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+
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+ An arg is `{"text", "max_diffs"}`, `{"number"}` or `{"latex"}`, and its kind says how it is
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+ compared: text within `max_diffs` edits, a number digit for digit, LaTeX by what it renders (or by its plain
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+ text, when plain text writes it without loss).
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+
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+ ## Install
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+
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+ ```bash
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+ uv pip install omni-parse-bench # the scorer: score(), no network, no API keys
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+ uv pip install 'omni-parse-bench[harness]' # + vendor adapters, to predict
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+ uv pip install 'omni-parse-bench[benchmark]' # + dataset access, to run the benchmark
157
+ ```
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+
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+ ## Run the benchmark
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+
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+ With `[benchmark]` installed, run parsers on the dataset (5 pages here). It shows the plan and asks before
162
+ calling any vendor, since calls cost money; each vendor needs its API key in the environment. A run is
163
+ resumable: stop it, and run the same command again to carry on.
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+
165
+ ```bash
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+ opb benchmark --out runs/ --limit 5 --providers datalab openai/gpt-5.6-sol
167
+ ```
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+
169
+ Each setting of a provider is its own run; here, two:
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+
171
+ ```bash
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+ opb benchmark --out runs/ --limit 5 --providers datalab --options '{"datalab": [{"mode": "balanced"}, {"mode": "accurate"}]}'
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+ ```
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+
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+ `--dataset` picks the dataset: a folder, or `hf://datasets/datalab-to/omni_parse_bench@<revision>` (this one, at `main`, by
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+ default). Corrections land on `main` as new commits; pin a revision to reproduce a result. Each run's
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+ `scores.jsonl` has one line per test, with what the test is beside its verdict, so a run can be compared by
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+ tag, suite or test type: see [`docs/API.md`](https://github.com/datalab-to/omni_parse_bench/blob/main/docs/API.md#asking-a-question).
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+
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+ ## Score
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+
182
+ Use in your own code: one page with three tests, written inline so the gold file's shape is visible
183
+ (`gold.read("gold/<page_id>.json")` reads a file).
184
+
185
+ ```python
186
+ from omni_parse_bench import gold, score, summarize
187
+
188
+ tests = gold.parse("example-p1", {"tests": [
189
+ {
190
+ "id": "t1",
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+ "test_type": "present",
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+ "output_type": "html",
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+ "derivation": "text_layer",
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+ "tags": ["Latn", "eng"],
195
+ "args": {"content": {"text": "Quarterly revenue rose 12%", "max_diffs": 1}},
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+ "settings": {},
197
+ },
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+ {
199
+ "id": "t2",
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+ "test_type": "order",
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+ "output_type": "html",
202
+ "derivation": "text_layer",
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+ "tags": ["Latn", "eng", "heading"],
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+ "args": {
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+ "before": {"text": "Summary", "max_diffs": 0},
206
+ "after": {"text": "Outlook", "max_diffs": 0},
207
+ },
208
+ "settings": {},
209
+ },
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+ {
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+ "id": "t3",
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+ "test_type": "table_cell",
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+ "output_type": "html",
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+ "derivation": "publisher_markup",
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+ "tags": ["table"],
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+ "args": {
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+ "cell": {"text": "4.2", "max_diffs": 0},
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+ "top_heading": {"text": "Q3", "max_diffs": 0},
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+ },
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+ "settings": {},
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+ },
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+ ]})
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+
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+ outputs = {
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+ "html": (
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+ "<h2>Summary</h2>"
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+ "<p>Quarterly revenue rose 12% on the year.</p>"
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+ "<table>"
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+ "<tr><th>Q2</th><th>Q3</th></tr>"
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+ "<tr><td>3.9</td><td>4.1</td></tr>"
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+ "</table>"
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+ "<h2>Outlook</h2>"
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+ ),
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+ }
235
+ verdicts = score(outputs, tests)
236
+ ```
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+
238
+ ```python
239
+ >>> verdicts
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+ [Verdict(id='t1', result='pass', why=''),
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+ Verdict(id='t2', result='pass', why=''),
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+ Verdict(id='t3', result='fail', why="no cell matching '4.2' in any table")]
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+
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+ >>> s = summarize(verdicts, tests)["html"]
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+ >>> s["score"], s["per_test_type"], s["per_tag"]["table"]
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+ (0.6666666666666666, {'present': 1.0, 'order': 1.0, 'table_cell': 0.0}, {'score': 0.0, 'tests': 1, 'pages': 1})
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+ ```
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+
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+ A verdict is `pass`, `fail`, `unsupported` (the parser can't produce that output) or `error` (grading
250
+ raised: a bug in the scorer, not the parser's fault), with `why` saying what went wrong. `unsupported`
251
+ and `error` verdicts are left out of every score.
252
+
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+ From the command line, with the outputs in a JSON file:
254
+
255
+ ```bash
256
+ opb score --pred outputs.json --gold gold/<page_id>.json
257
+ ```
258
+
259
+ ## Predict
260
+
261
+ With `[harness]` installed, predict with our provider adapters:
262
+
263
+ ```python
264
+ from omni_parse_bench import harness
265
+
266
+ record = harness.predict("datalab", "pages/<page_id>.pdf", {"html", "blocks"}, mode="balanced")
267
+ record["outputs"] # {"html": "...", "blocks": [...]}
268
+ record["errors"] # {output: why} for anything that failed
269
+ record["calls"] # what was sent, cost, wall time
270
+
271
+ score(record["outputs"], gold.read("gold/<page_id>.json"))
272
+ ```
273
+
274
+ From the command line:
275
+
276
+ ```bash
277
+ opb predict --provider openai/gpt-5.6-sol --page pages/<page_id>.pdf --wants html
278
+ opb providers # every provider, and its options
279
+ ```
280
+
281
+ ## How the tests were built
282
+
283
+ What a test is made of, where each part of it comes from (its derivation says which source gave its
284
+ answer), and how tags are decided: [`docs/building-tests.md`](https://github.com/datalab-to/omni_parse_bench/blob/main/docs/building-tests.md)
285
+ and [`docs/tagging.md`](https://github.com/datalab-to/omni_parse_bench/blob/main/docs/tagging.md) in the repository.
286
+
287
+ ## License
288
+
289
+ Each source's license, and the credit it requires, is a file in `licenses/`; each PubMed Central
290
+ article's credit is a row of `licenses/pmc_articles.csv`.
291
+
292
+ The pages are public documents, pages of public datasets, and copies of them we filled in or rebuilt. A dataset's license
293
+ covers what its makers could license; the documents it collected keep their owners' rights, and are
294
+ credited here to their source. Pages whose own license forbids redistribution or commercial use (a
295
+ PubMed Central article under CC BY-NC, for one) are left out. Two sources ask for non-commercial use
296
+ and are kept, as fair use, with our reasons in their files: the Ohio Department of Transportation
297
+ drawings (`ohio-dot-LICENSE.txt`), and the three Copiste d'un jour pages, whose writers consented to
298
+ non-commercial use (`htr-united-LICENSE.txt`).
299
+
300
+ The tests, and what we added to pages (form fills, handwriting), are Datalab's, under CC BY 4.0
301
+ (https://creativecommons.org/licenses/by/4.0/legalcode). Tests converted from olmOCR-bench and
302
+ fr-bench-pdf2md keep their licenses (ODC-BY 1.0 and MIT), and the tests on the three Copiste d'un
303
+ jour pages, which quote Wikipedia, are CC BY-SA 4.0 (`htr-united-LICENSE.txt`).
304
+
305
+ If you hold rights in a page and want it removed, open an issue at
306
+ https://github.com/datalab-to/omni_parse_bench/issues.
307
+
308
+ The scorer and harness are Apache 2.0.
309
+
310
+ | suites | pages | license file |
311
+ |---|---:|---|
312
+ | olmo_arxiv_math, olmo_long_tiny_text, olmo_multi_column, olmo_old_scans, olmo_old_scans_math, olmo_table_tests | 974 | `olmocr-bench-LICENSE.txt` |
313
+ | tables_derived | 528 | `pmc-LICENSE.txt`, with each article's credit in `pmc_articles.csv` |
314
+ | docparsing_images | 71 | `docparsingbench-LICENSE.txt` |
315
+ | parsebench_docs | 81 | `parsebench-LICENSE.txt` |
316
+ | realdoc_pdfs, realdoc_pages | 210 | `realdoc-bench-LICENSE.txt` |
317
+ | multiling_finepdfs | 158 | `finepdfs-LICENSE.txt` |
318
+ | rotation_skew | 142 | the file of the page it is a rotated copy of: `pmc-LICENSE.txt`, `parsebench-LICENSE.txt` or `realdoc-bench-LICENSE.txt` |
319
+ | forms_filled | 34 | `us-federal-forms-LICENSE.txt` and `cerfa-LICENSE.txt` |
320
+ | forms_federal | 31 | `us-federal-forms-LICENSE.txt` |
321
+ | multilingual | 42 | `cerfa-LICENSE.txt` and `nta-LICENSE.txt` |
322
+ | frbench_handwriting | 29 | `fr-bench-pdf2md-LICENSE.txt` and `cerfa-LICENSE.txt` |
323
+ | forms_documentcloud | 17 | `documentcloud-LICENSE.txt` |
324
+ | large_format, tiny_plans | 87 | `ohio-dot-LICENSE.txt` |
325
+ | handwriting_samples | 39 | `loc-catt-papers-LICENSE.txt`, `nls-marjory-fleming-LICENSE.txt` and `htr-united-LICENSE.txt` |
326
+ | synth_copies | 494 | the file of the page it is rebuilt from: `realdoc-bench-LICENSE.txt`, `parsebench-LICENSE.txt`, `docparsingbench-LICENSE.txt`, `olmocr-bench-LICENSE.txt` or `finepdfs-LICENSE.txt` |
327
+
328
+ ## Citation
329
+
330
+ ```bibtex
331
+ @misc{omniparsebench2026,
332
+ title = {Omni Parse Bench: A Document-Parsing Benchmark},
333
+ author = {{Datalab}},
334
+ year = {2026},
335
+ howpublished = {\url{https://github.com/datalab-to/omni_parse_bench}},
336
+ }
337
+ ```
338
+
339
+ Please also cite the upstream datasets whose pages or tests this benchmark uses:
340
+
341
+ - olmOCR-bench (Ai2): Poznanski J. et al. (2025). olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models;
342
+ - ParseBench (LlamaIndex): Zhang B. et al. (2026). ParseBench: A Document Parsing Benchmark for AI Agents;
343
+ - DocParsingBench (SoMark): SoMark (2026). DocParsingBench. https://huggingface.co/datasets/SoMarkAI/DocParsingBench;
344
+ - fr-bench-pdf2md (pulsia): pulsia (2026). Benchmarking Vision-Language Models for French PDF-to-Markdown Conversion;
345
+ - RealDoc-Bench and RealDoc-Bench-Layout (Extend AI): https://huggingface.co/datasets/Extend-AI/RealDoc-Bench; and
346
+ - FinePDFs (Hugging Face): https://huggingface.co/datasets/HuggingFaceFW/finepdfs.
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assets/languages.svg ADDED
assets/latency.svg ADDED
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