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allenai/olmOCR-bench

olmOCR-bench olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have. This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and structural information. Quick links: 📃 Paper 🛠️ Code 🎮 Demo Table 1. Distribution of Test Classes by Document Source Document Source Text Present Text… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-bench.

sourceHugging Faceodc-byupdated 7mo agoView on Hugging Face
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Dataset Card

olmOCR-bench

olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have. This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and structural information.

Quick links:

Table 1. Distribution of Test Classes by Document Source

Document SourceText PresentText AbsentReading OrderTableMathTotal
arXiv Math----2,9272,927
Headers Footers-753---753
Long Tiny Text442----442
Multi Column--884--884
Old Scans27970177--526
Old Scans Math----458458
Table Tests---1,020-1,020
Total7218231,0611,0203,3857,010

Table 2. Document source category breakdown

**Category****PDFs****Tests****Source****Extraction Method**
arXiv_math5222,927arXivDynamic programming alignment
oldscansmath36458Internet ArchiveScript-generated + manual rules
tables_tests1881,020Internal repositorygemini-flash-2.0
old_scans98526Library of CongressManual rules
headers_footers266753Internal repositoryDocLayout-YOLO + gemini-flash-2.0
multi_column231884Internal repositoryclaude-sonnet-3.7 + HTML rendering
longtinytext62442Internet Archivegemini-flash-2.0
Total1,4037,010Multiple sources

Evaluation Criteria

  • Text Presence: Checks if a short text segment (1–3 sentences) is correctly identified in the OCR output. Supports fuzzy matching and positional constraints (e.g., must appear in the first/last N characters). Case-sensitive by default.
  • Text Absence: Ensures specified text (e.g., headers, footers, page numbers) is excluded. Supports fuzzy matching and positional constraints. Not case-sensitive.
  • Natural Reading Order: Verifies the relative order of two text spans (e.g., headline before paragraph). Soft matching enabled; case-sensitive by default.
  • Table Accuracy: Confirms that specific cell values exist in tables with correct neighboring relationships (e.g., value above/below another). Supports Markdown and HTML, though complex structures require HTML.
  • Math Formula Accuracy: Detects the presence of a target equation by matching symbol layout (e.g., $\int$ to the left of $x$). Based on rendered bounding boxes and relative positioning.

📊 Benchmark Results by Document Source

**Model**ArXivBaseHdr/FtrTinyTxtMultColOldScanOldMathTablesOverall
GOT OCR52.794.093.629.942.022.152.00.248.3 ± 1.1
Marker v1.6.224.399.587.176.971.024.322.169.859.4 ± 1.1
MinerU v1.3.1075.496.696.639.159.017.347.460.961.5 ± 1.1
Mistral OCR API77.299.493.677.171.329.367.560.672.0 ± 1.1
GPT-4o (Anchored)53.596.893.860.669.340.774.570.069.9 ± 1.1
GPT-4o (No Anchor)51.596.794.254.168.940.975.569.168.9 ± 1.1
Gemini Flash 2 (Anchored)54.595.664.771.561.534.256.172.163.8 ± 1.2
Gemini Flash 2 (No Anchor)32.194.048.084.458.727.856.361.457.8 ± 1.1
Qwen 2 VL (No Anchor)19.755.588.96.88.317.131.724.231.5 ± 0.9
Qwen 2.5 VL (No Anchor)63.198.373.649.168.338.665.767.365.5 ± 1.2
Ours (No Anchor)72.198.191.680.578.543.774.771.576.3 ± 1.1
Ours (Anchored)75.699.093.481.779.444.575.170.277.4 ± 1.0

License

This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with AI2's Responsible Use Guidelines.