README.md

---
pretty_name: MWS Vision Bench
dataset_name: mws-vision-bench
language:
  - ru
license: cc-by-4.0
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 (Validation Set)

Top models evaluated on this validation dataset:

| Model | Overall | img→text | img→markdown | Grounding | KIE (JSON) | VQA |
|-------|---------|----------|--------------|-----------|------------|-----|
| **Claude-4.6-Opus** | 0.704 | 0.841 | 0.748 | 0.168 | 0.852 | 0.908 |
| **Gemini-2.5-pro** | 0.690 | 0.840 | 0.717 | 0.070 | 0.888 | 0.935 |
| **Gemini-3-flash-preview** | 0.681 | 0.836 | 0.724 | 0.051 | 0.845 | 0.950 |
| **Gemini-2.5-flash** | 0.672 | 0.886 | 0.729 | 0.042 | 0.825 | 0.879 |
| **Claude-4.5-Opus** | 0.670 | 0.809 | 0.720 | 0.131 | 0.799 | 0.889 |
| **Claude-4.5-Sonnet** | 0.669 | 0.741 | 0.660 | 0.459 | 0.727 | 0.759 |
| **GPT-5.2** | 0.663 | 0.799 | 0.656 | 0.173 | 0.855 | 0.835 |
| **Alice AI VLM dev** | 0.662 | 0.881 | 0.777 | 0.063 | 0.747 | 0.841 |
| **GPT-4.1-mini** | 0.659 | 0.863 | 0.735 | 0.093 | 0.750 | 0.853 |
| Cotype VL (32B 8 bit) | 0.649 | 0.802 | 0.754 | 0.267 | 0.683 | 0.737 |
| GPT-5-mini | 0.639 | 0.782 | 0.678 | 0.117 | 0.774 | 0.843 |
| Qwen3-VL-235B-A22B-Instruct | 0.623 | 0.812 | 0.668 | 0.050 | 0.755 | 0.830 |
| Qwen2.5-VL-72B-Instruct | 0.621 | 0.847 | 0.706 | 0.173 | 0.615 | 0.765 |
| GPT-5.1 | 0.588 | 0.716 | 0.680 | 0.092 | 0.670 | 0.783 |
| Qwen3-VL-8B-Instruct | 0.584 | 0.780 | 0.700 | 0.084 | 0.592 | 0.766 |
| Qwen3-VL-32B-Instruct | 0.582 | 0.730 | 0.631 | 0.056 | 0.708 | 0.784 |
| GPT-4.1 | 0.574 | 0.692 | 0.681 | 0.093 | 0.624 | 0.779 |
| Qwen3-VL-4B-Instruct | 0.515 | 0.699 | 0.702 | 0.061 | 0.506 | 0.607 |

*Scale: 0.0 - 1.0 (higher is better)*

**📝 Submit your model**: To evaluate on the private test set, contact [g.gaikov@mts.ai](mailto:g.gaikov@mts.ai)

---

## 💻 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**