shhdwi/olmocr-pre-rendered

olmOCR-bench Pre-Rendered

Pre-rendered PNG images of the olmOCR-bench benchmark dataset, ready for zero-setup evaluation of any OCR / vision model.

What This Is

The official olmOCR benchmark requires downloading 1,403 PDFs locally and rendering each page to a PNG image before sending it to a model. Every benchmark runner in the official repo does this same rendering step internally — see olmocr/data/renderpdf.py::render_pdf_to_base64png().

This dataset eliminates that setup entirely by hosting the pre-rendered images directly. The PNGs are rendered at target_longest_image_dim=2048 — the same default resolution used by the official olmOCR render_pdf_to_base64png() function and by the GPT-4o, Claude, and Gemini benchmark runners.

All files are accessible via direct URL, so you can evaluate any model by just pointing at these URLs — no local downloads, no PDF rendering tools, no dataset cloning.

Dataset Structure

The dataset has 7 subsets (one per benchmark category), each with a test split:

Subset	PDFs	Tests	Test Types
arxiv_math	522	2,927	math
headers_footers	266	753	absent
long_tiny_text	62	442	present
multi_column	231	884	order
old_scans	98	526	present, absent, order
old_scans_math	36	458	math
tables	188	1,020	table

Loading with datasets

from datasets import load_dataset

ds = load_dataset("shhdwi/olmocr-pre-rendered", "arxiv_math", split="test")
print(ds[0])  # {'image': <PIL.Image>, 'pdf_stem': '...', 'category': '...', ...}

Contents

Directory	Contents	Count
images/	Pre-rendered PNG images (page 1, 2048px longest dim) with metadata.jsonl per category	1,403 images
ground_truth/	JSONL test case files (from allenai/olmOCR-bench)	7,010 tests
predictions/	Published model prediction caches	1,403 .md files

Rendering Details

Each PDF page is rendered to PNG matching the official olmOCR benchmark process:

• Resolution: target_longest_image_dim = 2048 (longest side scaled to 2048px, aspect ratio preserved)
• Renderer: PyMuPDF (same pixel output as pdftoppm used in the official repo)
• Pages: Page 1 only (the benchmark tests only page 1 of each PDF)
• Naming: {pdf_stem}_pg1.png

This matches what the official benchmark runners do internally:

• run_chatgpt.py: render_pdf_to_base64png(pdf_path, target_longest_image_dim=2048)
• run_claude.py: render_pdf_to_base64png(pdf_path, target_longest_image_dim=2048)
• run_gemini.py: render_pdf_to_base64png(pdf_path, target_longest_image_dim=2048)
• run_server.py: render_pdf_to_base64png(pdf_path, target_longest_image_dim=1024) (for smaller models)

Quick Start

Evaluate any model with zero setup:

pip install litellm httpx

# Run on any litellm-supported model
python run_bench.py --model gpt-4o
python run_bench.py --model claude-sonnet-4-20250514
python run_bench.py --model gemini/gemini-2.0-flash

# Run specific categories only
python run_bench.py --model gpt-4o --categories arxiv_math headers_footers

# Evaluate published predictions (no API key needed)
python run_bench.py --evaluate nanonets-optimal-v4

Direct File Access

Every file is accessible via URL:

https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered/resolve/main/images/arxiv_math/2503.05390_pg14_pg1.png
https://huggingface.co/datasets/shhdwi/olmocr-pre-rendered/resolve/main/ground_truth/arxiv_math.jsonl

Attribution

Based on olmOCR-bench by Allen AI (paper). Licensed ODC-BY-1.0.