| --- |
| 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/test.jsonl |
| - config_name: 2026-q1-formulas-only |
| data_files: |
| - split: test |
| path: 2026-q1-formulas-only/test.jsonl |
| --- |
| |
| # PDF Parse Bench |
|
|
| [](https://github.com/phorn1/pdf-parse-bench) |
| [](https://pypi.org/project/pdf-parse-bench/) |
| [](https://arxiv.org/abs/2512.09874) |
| [](https://arxiv.org/abs/2603.18652) |
|
|
| 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 = {2512.09874}, |
| 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> |
|
|
