README.md

---
pretty_name: MWS Vision Bench
dataset_name: mws-vision-bench
language:
  - ru
license: mit
tags:
  - benchmark
  - multimodal
  - ocr
  - kie
  - grounding
  - vlm
  - business
  - russian
  - document
  - visual-question-answering
  - document-question-answering
task_categories:
  - visual-question-answering
  - document-question-answering
size_categories:
  - 1K<n<10K
annotations_creators:
  - expert-generated
dataset_creators:
  - MTS AI Research
papers:
  - title: "MWS Vision Bench: The First Russian Business-OCR Benchmark for Multimodal Models"
    authors: ["MTS AI Research Team"]
    year: 2025
    status: "in preparation"
    note: "Paper coming soon"
homepage: https://huggingface.co/datasets/MTSAIR/MWS-Vision-Bench
repository: https://github.com/mts-ai/MWS-Vision-Bench
organization: MTSAIR
---
# MWS-Vision-Bench

> 🇷🇺 *Русскоязычное описание ниже / Russian summary below.*

**MWS Vision Bench** — the first **Russian-language business-OCR benchmark** designed for multimodal large language models (MLLMs).  
This is the validation split - publicly available for open evaluation and comparison.  
🧩 **Paper is coming soon.**

🔗 **Official repository:** [github.com/mts-ai/MWS-Vision-Bench](https://github.com/mts-ai/MWS-Vision-Bench)  
🏢 **Organization:** [MTSAIR on Hugging Face](https://huggingface.co/MTSAIR)  
📰 **Article on Habr (in Russian):** [“MWS Vision Bench — the first Russian business-OCR benchmark”](https://habr.com/ru/companies/mts_ai/articles/953292/)

---

## 📊 Dataset Statistics

- **Total samples:** 1,302  
- **Unique images:** 400  
- **Task types:** 5  

---

## 🖼️ Dataset Preview

![Dataset Examples](preview.jpg)

*Examples of diverse document types in the benchmark: business documents, handwritten notes, technical drawings, receipts, and more.*

---

## 📁 Repository Structure

```
MWS-Vision-Bench/
├── metadata.jsonl       # Dataset annotations
├── images/              # Image files organized by category
│   ├── business/
│   │   ├── scans/
│   │   ├── sheets/
│   │   ├── plans/
│   │   └── diagramms/
│   └── personal/
│       ├── hand_documents/
│       ├── hand_notebooks/
│       └── hand_misc/
└── README.md            # This file
```

---

## 📋 Data Format

Each line in `metadata.jsonl` contains one JSON object:

```python
{
  "file_name": "images/image_0.jpg",   # Path to the image
  "id": "1",                           # Unique identifier
  "type": "text grounding ru",         # Task type
  "dataset_name": "business",          # Subdataset name
  "question": "...",                   # Question in Russian
  "answers": ["398", "65", ...]        # List of valid answers (as strings)
}
```

---

## 🎯 Task Types

| Task | Description | Count |
|------|--------------|-------|
| `document parsing ru` | Parsing structured documents | 243 |
| `full-page OCR ru` | End-to-end OCR on full pages | 144 |
| `key information extraction ru` | Extracting key fields | 119 |
| `reasoning VQA ru` | Visual reasoning in Russian | 400 |
| `text grounding ru` | Text–region alignment | 396 |

---

## Leaderboard

| Model | Overall | img→text | img→markdown | Grounding | KIE (JSON) | VQA |
|-------|---------|----------|--------------|-----------|------------|-----|
| **Gemini-2.5-pro** | **0.682** | 0.836 | 0.745 | 0.084 | 0.891 | 0.853 |
| **Gemini-2.5-flash** | **0.644** | 0.796 | 0.683 | 0.067 | 0.841 | 0.833 |
| **gpt-4.1-mini** | **0.643** | 0.866 | 0.724 | 0.091 | 0.750 | 0.782 |
| **Claude-4.5-Sonnet** | **0.639** | 0.723 | 0.676 | 0.377 | 0.728 | 0.692 |
| **Cotype VL (32B 8 bit)** | **0.639** | 0.797 | 0.756 | 0.262 | 0.694 | 0.685 |
| gpt-5-mini | 0.632 | 0.797 | 0.678 | 0.126 | 0.784 | 0.776 |
| Qwen2.5-VL-72B | 0.631 | 0.848 | 0.712 | 0.220 | 0.644 | 0.732 |
| gpt-5-mini (responses) | 0.594 | 0.743 | 0.567 | 0.118 | 0.811 | 0.731 |
| Qwen3-VL-30B-A3B | 0.589 | 0.802 | 0.688 | 0.053 | 0.661 | 0.743 |
| gpt-4.1 | 0.587 | 0.709 | 0.693 | 0.086 | 0.662 | 0.784 |
| Qwen3-VL-30B-A3B-FP8 | 0.583 | 0.798 | 0.683 | 0.056 | 0.638 | 0.740 |
| Qwen2.5-VL-32B | 0.577 | 0.767 | 0.649 | 0.232 | 0.493 | 0.743 |
| gpt-5 (responses) | 0.573 | 0.746 | 0.650 | 0.080 | 0.687 | 0.704 |
| Qwen2.5-VL-7B | 0.549 | 0.779 | 0.704 | 0.185 | 0.426 | 0.651 |
| gpt-4.1-nano | 0.503 | 0.676 | 0.672 | 0.028 | 0.567 | 0.573 |
| gpt-5-nano | 0.503 | 0.487 | 0.583 | 0.091 | 0.661 | 0.693 |
| Qwen2.5-VL-3B | 0.402 | 0.613 | 0.654 | 0.045 | 0.203 | 0.494 |
| Pixtral-12B-2409 | 0.342 | 0.327 | 0.555 | 0.026 | 0.325 | 0.475 |


## 💻 Usage Example

```python
from datasets import load_dataset

# Load dataset (authorization required if private)
dataset = load_dataset("MTSAIR/MWS-Vision-Bench", token="hf_...")

# Example iteration
for item in dataset:
    print(f"ID: {item['id']}")
    print(f"Type: {item['type']}")
    print(f"Question: {item['question']}")
    print(f"Image: {item['image_path']}")
    print(f"Answers: {item['answers']}")
```

---

## 📄 License

**MIT License**  
© 2024 MTS AI  

See [LICENSE](https://github.com/MTSAIR/multimodalocr/blob/main/LICENSE.txt) for details.

---

## 📚 Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{mwsvisionbench2024,
  title={MWS-Vision-Bench: Russian Multimodal OCR Benchmark},
  author={MTS AI Research},
  organization={MTSAIR},
  year={2025},
  url={https://huggingface.co/datasets/MTSAIR/MWS-Vision-Bench},
  note={Paper coming soon}
}
```

---

## 🤝 Contacts

- **Team:** [MTSAIR Research](https://huggingface.co/MTSAIR)  
- **Email:** [g.gaikov@mts.ai](mailto:g.gaikov@mts.ai)

---

## 🇷🇺 Краткое описание

**MWS Vision Bench** — первый русскоязычный бенчмарк для бизнес-OCR в эпоху мультимодальных моделей.  
Он включает 1302 примера и 5 типов задач, отражающих реальные сценарии обработки бизнес-документов и рукописных данных.  
Датасет создан для оценки и развития мультимодальных LLM в русскоязычном контексте.  
📄 *Научная статья в процессе подготовки (paper coming soon).*

---

**Made with ❤️ by MTS AI Research Team**