Model reference · open weights

PP-OCRv5_mobile_det_onnx

LLMs PaddlePaddle Image→text 1 build Open weights 17k dl/mo

PP-OCRv5_mobile_det_onnx is an open-weight language model from Baidu.

  • PP-OCRv5_mobile_det_onnx is an image-to-text model developed by Baidu for detecting text in diverse scenarios.
  • It supports English and Chinese languages and is distributed under the apache-2.0 licence.
  • The model is designed to handle complex layouts, varying text sizes, and challenging backgrounds for applications such as document analysis and scene text detection.

Summary of the PaddlePaddle/PP-OCRv5_mobile_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_mobile_det_onnx

Read the model card

Introduction

PP-OCRv5_mobile_det is one of the PP-OCRv5_det series, the latest generation of text detection models developed by the PaddleOCR team. It aims to efficiently and accurately 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
0.7440.7770.9050.9100.8230.5810.7270.7210.5750.6470.8270.5250.770

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_mobile_det --engine onnxruntime

Python API Usage

from paddleocr import TextDetection

model = TextDetection(
    model_name="PP-OCRv5_mobile_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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