Model reference · open weights

PP-OCRv5_server_det_onnx

LLMs PaddlePaddle Image→text 1 build Open weights 514 dl/mo

PP-OCRv5_server_det_onnx is an open-weight language model from Baidu.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • PP-OCRv5_server_det_onnx is an image-to-text model developed by Baidu for detecting text in diverse scenarios, including handwriting, vertical, rotated, and curved text.
  • It supports Simplified Chinese, Traditional Chinese, English, and Japanese, and is designed for applications such as document analysis and scene text detection.
  • The model is distributed under the apache-2.0 license.

Summary of the PaddlePaddle/PP-OCRv5_server_det_onnx model card, 2026-10-01

What it is

Released byBaidu
Released2026-05-26

From the model card

What Baidu says about PP-OCRv5_server_det_onnx

Read the model card

Introduction

PP-OCRv5_server_det is one of the PP-OCRv5_det series, the latest generation of text detection models developed by the PaddleOCR team. Designed for high-performance applications, it supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow:

Handwritten ChineseHandwritten EnglishPrinted ChinesePrinted EnglishTraditional ChineseAncient TextJapaneseGeneral ScenarioPinyinRotationDistortionArtistic TextAverage
0.8030.8410.9450.9170.8150.6760.7720.7970.6710.80.8760.6730.827

Model Usage

Install Dependencies

pip install -U paddleocr
pip install -U onnxruntime-gpu

CLI Usage

paddleocr text_detection -i ./demo.png --model_name PP-OCRv5_server_det --engine onnxruntime

Python API Usage

from paddleocr import TextDetection

model = TextDetection(
    model_name="PP-OCRv5_server_det",
    engine="onnxruntime",
)
output = model.predict("./demo.png", batch_size=1)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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