Hugging Face's logo

Datasets:
MTSAIR
/
MWS-Vision-Bench

Tasks:
Visual Question Answering
Document Question Answering
Modalities:
Image
Text
Document
Formats:
parquet
Languages:
Russian
Size:
1K - 10K
Tags:
benchmark
multimodal
ocr
kie
grounding
vlm
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
2
MWS-Vision-Bench / README.md
GeorgyG's picture
GeorgyG
Add language config usage examples to README
df378e2 verified
preview code
|
raw
history blame contribute delete
8.84 kB
metadata 
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
dataset_info:
  - config_name: default
    features:
      - name: image
        dtype: image
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
    splits:
      - name: train
        num_bytes: 262810803
        num_examples: 1302
    download_size: 238315183
    dataset_size: 262810803
  - config_name: en
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 252074099
        num_examples: 1302
    download_size: 251760293
    dataset_size: 252074099
  - config_name: zh
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: dataset_name
        dtype: string
      - name: question
        dtype: string
      - name: answers
        list: string
      - name: image
        dtype: image
    splits:
      - name: train
        num_bytes: 252019805
        num_examples: 1302
    download_size: 251756853
    dataset_size: 252019805
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
  - config_name: zh
    data_files:
      - split: train
        path: zh/train-*

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.

Language Configs

This dataset provides three Hugging Face configs for the same benchmark split:

  • default — Russian questions
  • en — English questions
  • zh — Chinese questions
from datasets import load_dataset

vision_ru = load_dataset("MTSAIR/MWS-Vision-Bench")
vision_en = load_dataset("MTSAIR/MWS-Vision-Bench", "en")
vision_zh = load_dataset("MTSAIR/MWS-Vision-Bench", "zh")

🔗 Official repository: github.com/mts-ai/MWS-Vision-Bench
🏢 Organization: MTSAIR on Hugging Face
📰 Article on Habr (in Russian): “MWS Vision Bench — the first Russian business-OCR benchmark”

Update — February 16, 2026

New: VQA category update

We updated the Reasoning VQA (ru) category to improve evaluation robustness. Revised questions and answers only (images remain unchanged). Results are not directly comparable to versions prior to Feb 16, 2026 - for VQA and overall columns.

This update improves reliability of reasoning-based evaluation while keeping the benchmark structure intact.

📊 Dataset Statistics

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

🖼️ Dataset Preview

Dataset Examples

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:

{
  "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

💻 Usage Example 

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 for details.

📚 Citation

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

@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

🇷🇺 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