Datasets:

qihoo360
/

InduOCRBench

InduOCRBench is an OCR benchmark for industrial RAG systems, covering 11 challenging document types observed in real-world enterprise workflows. It addresses the gap between traditional character-level OCR metrics and actual downstream RAG utility, evaluating OCR robustness in terms of both transcription fidelity and end-to-end retrieval performance.

Key Features:

Real-world scenarios: Data sampled from 10,000 documents spanning 12 industries.
Scale and diversity: Contains 570 PDF documents and 3,402 pages covering 11 challenge types + 1 Normal category.
High-quality annotations: Fine-grained Hybrid Markdown annotations (Markdown + HTML tables + LaTeX formulas + style tags), with a 3-stage human-in-the-loop quality control achieving 98% accuracy.
Dual evaluation tracks: OCR fidelity (character/structure metrics) and RAG impact (end-to-end retrieval + generation accuracy).

Key findings:

Models achieving near-perfect scores on standard benchmarks (OmniDocBench) decline sharply on InduOCRBench (e.g., PP-StructureV3 drops 26.4 points, PaddleOCR-VL drops 14.7 points).
High OCR accuracy does not necessarily translate into strong downstream RAG performance. VisualStyle documents achieve 82.9% OCR accuracy yet only 52.8% RAG accuracy — a 30.1-point discrepancy.
OCR-induced information loss is a strong and stable upstream limiting factor across all OCR-first RAG architectures.

The benchmark releases two evaluation tracks:

OCR Fidelity Evaluation — character/structure-level metrics comparing OCR output to ground-truth Markdown (ocr_data/).
RAG Impact Evaluation — end-to-end pipeline evaluation measuring how OCR quality affects retrieval and answer accuracy (RAG_eval/).

📊 Dataset Statistics

Statistic	Value
Total documents	570
Total pages	3,402
Industries covered	11 challenge types + 1 Normal
QA pairs (RAG eval)	2071
Annotation format	Hybrid Markdown (Markdown + HTML tables + LaTeX formulas + style)

11 challenge document types including:

ComplexBackground
HighPixel
UltraLong
MultiColumn
UltraWide
HistoryBooks
Handwriting
MultiFont
VisualStyle
Watermark
CrosspageTable

📂 Dataset Structure

The dataset is stored in the ocr_data directory, containing original files and annotations in two formats:

InduOCRBench/
├── ocr_data/
│   ├── pdf.zip           # Original PDF documents (570 files, 3402 pages)
│   ├── md.zip            # [Recommended] Ground-truth Markdown for OCR evaluation
│   └── md_original.zip   # Full-fidelity annotations preserving all visual style tags
│
├── RAG_eval/
│   ├── QA_pairs.jsonl    # QA pairs for RAG pipeline evaluation
│   └── doc_md/           # Ground-truth Markdown files referenced by QA_pairs.jsonl
│
├── README.md
└── README_zh-CN.md

md_original: Full-fidelity Markdown annotations preserving all visual style tags (e.g., font, color, alignment, layout). Suitable for studies requiring high-fidelity document reconstruction.
md: Style-stripped Markdown annotations containing only textual content. This version serves as the standard Ground Truth for OCR evaluation to ensure fair comparison.
doc_md: Hybrid Markdown annotations for RAG construction. Style information is preserved for VisualStyle documents while removed for other document types. This version is designated as the standard Ground Truth for RAG indexing and QA evaluation.

🚀 OCR Evaluation

This benchmark uses the md2md method from OmniDocBench for evaluation. For details, see: https://github.com/opendatalab/OmniDocBench/tree/main

Evaluation Result

Table 1. OCR fidelity evaluation on InduOCRBench using md2md metrics

Model Type	Methods	Size	Overall↑	Text^EDS↑	Formula^CDM↑	Table^TEDS↑	Table^TEDS-S↑	Read Order^EDS↑
Specialized VLMs	PaddleOCR-VL-1.5	0.9B	79.01	88.33	75.3	73.41	77.27	85.3
	PaddleOCR-VL	0.9B	78.24	88.1	74.6	72.03	75.87	85.6
	Logics-Parsing-v2	4B	75.71	84.94	72.3	69.90	76.17	88.9
	MinerU2.5-Pro	1.2B	74.47	81.63	75.8	65.99	70.46	79.1
	FireRed-OCR	2B	74.09	87.9	72.4	61.98	66.45	85.8
	MinerU2.5	1.2B	72.50	81.8	75.4	60.31	63.10	84.4
	GLM-OCR	0.9B	68.64	63.18	72.1	70.64	76.72	77.2
	hunyuan-ocr	0.9B	68.08	86.1	65.6	52.53	58.34	85.7
	deepseek-ocr	1.2B	61.46	75.5	61.8	47.07	49.31	81.8
General VLMs	Gemini-2.5 Pro	-	74.53	83.1	77.2	63.29	67.28	81.1
	Qwen3-VL-235B	235B	70.91	83.3	74.8	54.63	59.43	82.1
	Ovis2.6-30B-A3B	30B	59.34	60.2	65.8	52.03	57.00	64.4
	GPT-4o	-	52.01	60.8	58.1	37.15	43.83	70.0
Pipeline Tools	Mineru2-pipeline	-	66.54	80.1	63.2	56.32	62.05	81.3
Pipeline Tools	PP-StructureV3	-	60.32	78.2	53.7	49.07	62.06	79.1

Evaluation Setup

To ensure maximum fairness, please follow these settings:

Ground Truth: Please use the Markdown files extracted from ocr_data/md.zip as the benchmark.
Metric: Use the md2md evaluation metric to calculate similarity scores.

Note: Although md_original is provided, for standard leaderboard evaluations, please uniformly use the data under the md directory to align with the evaluation standards.

📝 Usage

Download and extract the data:
```
cd ocr_data
unzip pdf.zip
unzip md.zip
```
Run your model to perform inference on the documents in the pdf directory, generating prediction results in Markdown format.
Use the evaluation script to compare your prediction results with the Ground Truth under the md directory.

RAG Impact Evaluation

The RAG evaluation track measures how OCR quality affects end-to-end retrieval-augmented generation performance, going beyond character-level metrics to capture structural and semantic preservation.

RAG Evaluation Data

The RAG_eval/ directory contains:

QA_pairs.jsonl: 2,071 QA pairs covering all 11 document challenge types.
doc_md/: Ground-truth Markdown files for RAG indexing, using the doc_md format (style information preserved for VisualStyle document types, removed for others).

Each QA entry has the following fields:

{
  "doc_type": "cross_page_table",
  "filename": "cross_page_table_1.md",
  "title": "Document title",
  "file_path": "RAG_eval/doc_md/cross_page_table_1.md",
  "question_category": "...",
  "question": "...",
  "answer": "...",
  "evidence": "..."
}

RAG Pipeline Setup

We adopt the FlashRAG Naive pipeline with the following configuration:

Component	Setting
Embedding	BGE-M3
Retrieval	Dense, Flat index, top-100
Reranking	BGE-Rerank-V2-M3, top-10
Generation	ChatGPT-5
Chunking	HTML tree structure, max 256 tokens
Evaluation	RAGAS framework (GPT-OSS-120B as judge)

RAG Evaluation Metrics

Context Recall: Measures whether retrieved passages contain evidence supporting the ground-truth answer.
Answer Accuracy: Evaluates the correctness of the generated answer relative to the ground truth.

Key RAG Findings

Document Type	OCR Accuracy	RAG Accuracy	Gap
VisualStyle	82.9%	52.8%	-30.1 pts (blind spot)
CrosspageTbl	40.7%	63.8%	+23.1 pts (LLM compensates)
UltraWide	28.1%	49.1%	low-low (structural failure)
MultiFont	97.2%	97.5%	≈0 (aligned)

High OCR accuracy does not guarantee strong RAG performance. VisualStyle documents demonstrate the largest OCR–RAG discrepancy: despite 82.9% character-level accuracy, only 52.8% RAG accuracy is achieved because OCR strips visual formatting cues (strikethroughs, color emphasis) that encode critical semantics.

📄 License

This project is released under an open-source license. Please comply with relevant laws and regulations when using this dataset. The data is for research and academic purposes only.

Acknowledgement

Thank OmniDocBench for OCR metric calculation.
Thank FlashRAG for the RAG pipeline framework.
Thank ragas for the RAG metrics evaluation.

🖊️ Citation

If you use InduOCRBench in your research, please consider citing:

@misc{induocrbench,
  title={When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation},
  author={Lin Sun and Wangdexian and Jingang Huang and Linglin Zhang and Change Jia and Zhengwei Cheng and Xiangzheng Zhang},
  year={2026},
  eprint={2605.00911},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2605.00911},
}