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README.md

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+---
+license: mit
+task_categories:
+  - image-to-text
+  - document-question-answering
+language:
+  - en
+tags:
+  - pdf-parsing
+  - ocr
+  - benchmark
+  - mathematical-formulas
+  - tables
+  - llm-as-a-judge
+size_categories:
+  - n<1K
+configs:
+  - config_name: 2026-q1-tables-only
+    data_files:
+      - split: test
+        path: 2026-q1-tables-only/ground_truth/*.json
+  - config_name: 2026-q1-formulas-only
+    data_files:
+      - split: test
+        path: 2026-q1-formulas-only/ground_truth/*.json
+---
+
+# PDF Parse Bench
+
+Benchmark for evaluating how effectively PDF parsing solutions extract **mathematical formulas** and **tables** from documents.
+
+We generate synthetic PDFs with diverse formatting scenarios, parse them with different parsers, and score the extracted content using **LLM-as-a-Judge**. This semantic evaluation approach [substantially outperforms traditional metrics](https://github.com/phorn1/pdf-parse-bench#why-llm-as-a-judge) in agreement with human judgment.
+
+## Leaderboard (2026-Q1)
+
+Results are based on two benchmark datasets, each containing 100 synthetic PDFs:
+
+| Parser | Tables | Formulas |
+|--------|--------|----------|
+| [Gemini 3 Flash](https://deepmind.google/models/gemini/flash/) | 9.50 | 9.79 |
+| [LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B) | 9.08 | 9.57 |
+| [Mistral OCR](https://mistral.ai/) | 8.89 | 9.48 |
+| [dots.ocr](https://github.com/rednote-hilab/dots.ocr) | 8.73 | 9.55 |
+| [Mathpix](https://mathpix.com/) | 8.53 | 9.66 |
+| [Chandra](https://huggingface.co/datalab-to/chandra) | 8.43 | 9.45 |
+| [Qwen3-VL-235B](https://github.com/QwenLM/Qwen3-VL) | 8.43 | 9.84 |
+| [MonkeyOCR-pro-3B](https://github.com/Yuliang-Liu/MonkeyOCR) | 8.39 | 9.50 |
+| [GLM-4.5V](https://github.com/zai-org/GLM-V) | 7.98 | 9.37 |
+| [GPT-5 mini](https://openai.com/) | 7.14 | 5.57 |
+| [Claude Sonnet 4.6](https://docs.anthropic.com/en/docs/about-claude/models) | 7.02 | 8.50 |
+| [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) | 6.92 | 9.21 |
+| [PP-StructureV3](https://github.com/PaddlePaddle/PaddleOCR) | 6.86 | 9.59 |
+| [Gemini 2.5 Flash](https://deepmind.google/models/gemini/flash/) | 6.85 | 6.51 |
+| [MinerU2.5](https://mineru.net/) | 6.49 | 9.32 |
+| [GPT-5 nano](https://openai.com/) | 6.48 | 4.78 |
+| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | 5.75 | 8.97 |
+| [PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) | 5.39 | 8.47 |
+| [PyMuPDF4LLM](https://github.com/pymupdf/PyMuPDF4LLM) | 5.25 | 4.53 |
+| [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0) | 5.13 | 8.01 |
+| [olmOCR-2-7B](https://github.com/allenai/olmocr) | 4.05 | 9.35 |
+| [GROBID](https://github.com/kermitt2/grobid) | 2.10 | 7.01 |
+
+All scores are **LLM-as-a-Judge** ratings on a 0–10 scale, judged by Gemini 3 Flash via OpenRouter.
+
+## Datasets
+
+- **`2026-q1-tables-only`** — 100 PDFs with 451 tables (simple, moderate, complex)
+- **`2026-q1-formulas-only`** — 100 PDFs with 1413 inline + 657 display-mode mathematical formulas
+
+PDFs are generated synthetically using LaTeX with randomized parameters (document class, fonts, margins, column layout, line spacing). Since PDFs are generated from LaTeX source, ground truth is obtained automatically.
+
+## How to Evaluate Your Parser
+
+```bash
+pip install pdf-parse-bench
+```
+
+See the full evaluation guide at **[github.com/phorn1/pdf-parse-bench](https://github.com/phorn1/pdf-parse-bench)**.
+
+## Why LLM-as-a-Judge?
+
+Rule-based metrics correlate poorly with human judgment. We validated this in two human annotation studies:
+
+- **[formula-metric-study](https://github.com/phorn1/formula-metric-study)** — 750 human ratings: text metrics r = 0.01, CDM r = 0.31, LLM judges r = 0.74–0.82
+- **[table-metric-study](https://github.com/phorn1/table-metric-study)** — 1,500+ human ratings: rule-based (TEDS, GriTS) top at r = 0.70, LLM judges r = 0.94
+
+## Citation
+
+```bibtex
+@misc{horn2025formulabench,
+    title = {Benchmarking Document Parsers on Mathematical Formula Extraction from PDFs},
+    author = {Horn, Pius and Keuper, Janis},
+    year = {2025},
+    eprint = {2511.10390},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV},
+    url = {https://arxiv.org/abs/2512.09874}
+}
+
+@misc{horn2026tablebench,
+    title = {Benchmarking PDF Parsers on Table Extraction with LLM-based Semantic Evaluation},
+    author = {Horn, Pius and Keuper, Janis},
+    year = {2026},
+    eprint = {2603.18652},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV},
+    url = {https://arxiv.org/abs/2603.18652}
+}
+```
+
+## Acknowledgments
+
+This work has been supported by the German Federal Ministry of Research, Technology and Space (BMFTR) in the program "Forschung an Fachhochschulen in Kooperation mit Unternehmen (FH-Kooperativ)" within the joint project **LLMpraxis** under grant 13FH622KX2.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/phorn1/pdf-parse-bench/main/assets/BMFTR_logo.png" alt="BMFTR" width="150" />
+  <img src="https://raw.githubusercontent.com/phorn1/pdf-parse-bench/main/assets/HAW_logo.png" alt="HAW" width="150" />
+</p>