--- 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 🇷🇺 *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/) --- ## 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](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**