docs: Add dataset card and benchmark tags metadata

README.md

@@ -1,3 +1,104 @@
 ---
-license: apache-2.0
+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
+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
+```