Model reference · open weights

PP-OCRv5_server_det_onnx

Available as managed deployment LLMs PaddlePaddle Image→text 1 variants 514 dl/mo

PP-OCRv5_server_det_onnx is an open-weight language model from PaddlePaddle. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byBaidu
Published underPaddlePaddle
TypeLanguage models
TaskImage→text
Runs withPaddleOCR
Released2026-05-26
Popularity514 downloads / month
LicenceOpen weights

About

What PP-OCRv5_server_det_onnx is

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:

Read the full model card
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")

From the published model card. Full card on the HuggingFace links in the sidebar.

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.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys pp-ocrv5-server-det-onnx for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (pp-ocrv5-server-det-onnx below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"pp-ocrv5-server-det-onnx","messages":[{"role":"user","content":"Hello"}]}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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