| --- |
| 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 |
| ``` |
| |
