Model reference · open weights

PP-OCRv5_mobile_rec_onnx

LLMs PaddlePaddle Image→text 1 build Open weights 3k dl/mo

PP-OCRv5_mobile_rec_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_mobile_rec_onnx is an image-to-text model developed by Baidu for text line recognition.
  • It supports English and Chinese, including handwritten, vertical, and complex text scenarios.
  • The model is distributed under the apache-2.0 license.

Summary of the PaddlePaddle/PP-OCRv5_mobile_rec_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_rec_onnx

Read the model card

Introduction

PP-OCRv5_mobile_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. The key accuracy metrics are as follow:

Handwritten ChineseHandwritten EnglishPrinted ChinesePrinted EnglishTraditional ChineseAncient TextJapaneseGeneral ScenarioPinyinRotationDistortionArtistic TextAverage
0.41660.49440.86050.87530.71990.57860.75770.55700.77030.72480.80890.53980.8015

Note: If any character (including punctuation) in a line is incorrect, the entire line is marked as wrong. This ensures higher accuracy in practical applications.

Model Usage

Install Dependencies

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

CLI Usage

paddleocr text_recognition -i ./demo.png --model_name PP-OCRv5_mobile_rec --engine onnxruntime

Python API Usage

from paddleocr import TextRecognition

model = TextRecognition(
    model_name="PP-OCRv5_mobile_rec",
    engine="onnxruntime",
)
output = model.predict("./demo.png", batch_size=1)
for res in output:
    res.print()
    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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