MonkeyOCR-B-Parsing

News

• 2026.09.07 🚀 MonkeyOCRv2-Parsing is now supported by RAGFlow as a PDF parser. Thanks to the RAGFlow team and community for the support!
• 2026.08.22 🖥️ We now support running MonkeyOCRv2-Parsing on CPU. See the CPU support instructions.
• 2026.08.17 ⚡ Releasing training and evaluation instructions for Recognition, Detection, Overlapping Text Segmentation and Formula.
• 2026.07.24 ⚡ We released MonkeyOCRv2-B-Parsing-DFlash, enabling vLLM serving with DFlash for up to 2× faster inference.
• 2026.07.22 🏆 MonkeyOCRv2-B-Parsing ranks #1 among evaluated open-source models on the official MDPBench Leaderboard, achieving 83.3 overall across 17 languages, including digital-born and photographed documents.
• 2026.07.21 📦 We release MonkeyDoc v2, an open multilingual corpus for document-oriented pretraining. We hope it can serve as a shared data foundation for more transparent, reproducible, and fair comparisons in Document AI.
• 2026.07.14 🚀 We release MonkeyOCRv2, including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.

Use MonkeyOCRv2 as a Vision Backbone

MonkeyOCRv2 is released as a standalone, document-native vision encoder. It can be integrated into different OCR and document AI systems as a visual backbone.

The current release has been evaluated on document parsing, document understanding, text recognition, formula recognition, text detection, document tampering detection, and overlapping-text segmentation.

Beyond these evaluated tasks, the encoder may also be useful for text-rich scenarios such as scientific papers, historical documents, medical reports, charts and tables, and remote-sensing maps or reports. We welcome community exploration of these directions.

from transformers import AutoModel

encoder = AutoModel.from_pretrained(
    "zenosai/MonkeyOCRv2-B",
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
)

See the Vision Encoder Quick Start for installation and feature-extraction examples. If you adapt MonkeyOCRv2 to a new task or domain, feel free to open an issue or pull request and share the results.

MonkeyDoc v2

MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. The open-sourcing of MonkeyDoc v2 is still underway. So far, we have released 52 million synthetic samples and 52 million real-world samples. You can download the full dataset as follows:

pip install modelscope
modelscope download --dataset zenosai/MonkeyDocv2 --local_dir ./MonkeyDocv2

After processing and compression, downloading the dataset currently requires approximately 10 TB of disk space. We recommend having at least 11 TB of available storage before starting the download to ensure sufficient space throughout the process.

Model Zoo

1. Vision Encoder

2. Document Parsing Model

3. Document Understanding Model

Quick Start

What trust_remote_code=True loads is listed in docs/trust_remote_code.md.

Vision Encoder

1. Install

Install transformers and flash attention:

conda create -n MonkeyOCRv2 python=3.11
conda activate MonkeyOCRv2
pip install torch==2.8.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
pip install transformers==4.57.1
pip install accelerate==1.11.0
pip install qwen_vl_utils==0.0.14
pip install flash-attn==2.8.3 --no-build-isolation

2. Download Model Weights

Download our model from Huggingface.

python download_model.py -n MonkeyOCRv2-B # or MonkeyOCRv2-S / MonkeyOCRv2-AS

You can also download our model from ModelScope.

pip install modelscope
python download_model.py -t modelscope -n MonkeyOCRv2-B # or MonkeyOCRv2-S / MonkeyOCRv2-AS

3. Extract Image Feature

cd vision
# For MonkeyOCRv2-B and MonkeyOCRv2-S
python extract_feature.py -m ../model_weight/MonkeyOCRv2-B -i ../images_test/ar.JPEG
# For MonkeyOCRv2-AS
python extract_feature_vitae.py -m ../model_weight/MonkeyOCRv2-AS -i ../images_test/ar.JPEG

Document Parsing

1. Install

Install vLLM following its official guide:

conda create -n MonkeyOCRv2Parsing python=3.11
conda activate MonkeyOCRv2Parsing
pip install uv
uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.25.1/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r parsing/requirements.txt

To use DFlash for faster inference, vLLM 0.25.1 is required, which depends on CUDA 12.9 or later.

If your system does not support CUDA 12.9, you can instead install vLLM 0.11.2 (without DFlash support)