PP DocLayoutV2 EU AI Act Compliance Profile

Introduction

PP-DocLayoutV2 is a dedicated lightweight model for layout analysis, focusing specifically on element detection, classification, and reading order prediction.

Model Architecture

PP-DocLayoutV2 is composed of two sequentially connected networks. The first is an RT-DETR-based detection model that performs layout element detection and classification. The detected bounding boxes and class labels are then passed to a subsequent pointer network, which is responsible for ordering these layout elements.

Usage

Install Dependencies

Install PaddlePaddle and PaddleOCR:

python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
python -m pip install -U "paddleocr[doc-parser]"
python -m pip install https://paddle-whl.bj.bcebos.com/nightly/cu126/safetensors/safetensors-0.6.2.dev0-cp38-abi3-linux_x86_64.whl

For Windows users, please use WSL or a Docker container.

Basic Usage

Python API usage:

from paddleocr import LayoutDetection

model = LayoutDetection(model_name="PP-DocLayoutV2")
output = model.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout.jpg", batch_size=1, layout_nms=True)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

For more usage details and parameter explanations, see the documentation.

Citation

If you find PaddleOCR-VL helpful, feel free to give us a star and citation.

@misc{cui2025paddleocrvlboostingmultilingualdocument,
      title={PaddleOCR-VL: Boosting Multilingual Documen

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