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dataset_info:
features:
- name: pdf_filename
dtype: string
- name: page_number
dtype: int64
- name: test_type
dtype: string
- name: text
dtype: string
- name: case_sensitive
dtype: bool
- name: formula
dtype: string
- name: first_text
dtype: string
- name: second_text
dtype: string
splits:
- name: arxiv_math
num_examples: 850
- name: headers_footers
num_examples: 830
- name: table_tests
num_examples: 830
- name: multi_column
num_examples: 830
- name: old_scans
num_examples: 830
- name: long_tiny_text
num_examples: 830
configs:
- config_name: default
data_files:
- split: arxiv_math
path: bench_data/arxiv_math.jsonl
- split: headers_footers
path: bench_data/headers_footers.jsonl
- split: table_tests
path: bench_data/table_tests.jsonl
- split: multi_column
path: bench_data/multi_column.jsonl
- split: old_scans
path: bench_data/old_scans.jsonl
- split: long_tiny_text
path: bench_data/long_tiny_text.jsonl
tags:
- ocr
- document-understanding
- benchmark
- pdf
- vlm
- multimodal
- document
- text
license: odc-by
pretty_name: ArenaOCR
---
# ArenaOCR Benchmark
**ArenaOCR** is a highly rigorous, unit-test-driven Optical Character Recognition (OCR) and Document Understanding benchmark designed to assess the performance of Vision-Language Models (VLMs) and advanced OCR systems on extremely challenging real-world layouts.
Replicating the design paradigm and schema structure of `allenai/olmOCR-bench`, ArenaOCR shifts away from traditional "fuzzy" metrics (like character error rate, edit distance, or BLEU/ROUGE) and instead evaluates document transcripts using **machine-verifiable, deterministic unit tests** (e.g. math formula accuracy, column order preservation, header/footer suppression, and noise-tolerant transcription).
---
## Dataset Splits & Tasks
ArenaOCR contains **5,000 unique, procedurally generated PDF documents** and their corresponding JSONL unit tests split across 6 key difficulty divisions:
1. **`arxiv_math` (850 samples):** Evaluation of complex, multi-level academic LaTeX mathematical equations, featuring nested fractions, integrals, sums, Greek characters, and matrices.
2. **`headers_footers` (830 samples):** Assesses whether OCR systems can successfully isolate the document's central body text while discarding page-margin metadata like running headers, page counts, and publication tags.
3. **`table_tests` (830 samples):** Complex multi-column/multi-row layouts featuring cell merges (`SPAN`), missing cell boundaries, alternating shading, and dense finance/science alphanumeric matrices.
4. **`multi_column` (830 samples):** 2-column or 3-column academic article structures. Evaluates reading order preservation, verifying that the OCR reads columns vertically rather than leaking text horizontally across separators.
5. **`old_scans` (830 samples):** Simulates degraded photocopy text sheets from vintage manuscripts, featuring random speckle noise, page skew, faded inks, and streaking lines.
6. **`long_tiny_text` (830 samples):** Exceedingly dense legal terms and conditions (TOS/NDA agreements) utilizing minuscule (4.5pt - 5.5pt) font sizes to test transcription precision.
---
## Dataset Schema
Each JSONL unit test entry contains:
- `pdf_filename` (string): Relative path to the PDF file (e.g., `bench_data/pdfs/arxiv_math/arxiv_math_0001.pdf`).
- `page_number` (int): Page number within the document (always `1` for single-page benchmark pages).
- `test_type` (string): The verification logic applied:
- `math_formula`: LaTeX comparison of mathematical expressions.
- `text_absence`: Verifies that margins or header information were excluded.
- `text_presence`: Substring search validating target text extraction.
- `reading_order`: Checks if `first_text` occurs in the transcript before `second_text`.
- `text` (string, optional): String parameter for presence/absence checks.
- `case_sensitive` (bool, optional): Determines case matching constraints for presence/absence.
- `formula` (string, optional): Exact LaTeX ground-truth target.
- `first_text` (string, optional): Anchoring phrase that must appear earlier.
- `second_text` (string, optional): Anchoring phrase that must appear later.
---
## Local Evaluation
A local evaluation script `eval_bench.py` is included in the repository. Running the following command will evaluate model transcripts saved in a `./predictions` directory against our benchmark unit tests:
```bash
python eval_bench.py --predictions ./predictions
```
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