Model reference · open weights
PP-OCRv5_server_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.
Summary of the PaddlePaddle/PP-OCRv5_server_rec_onnx model card, 2026-10-01
What it is
| Released by | Baidu |
|---|---|
| Released | 2026-05-26 |
From the model card
PP-OCRv5_server_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 Chinese | Handwritten English | Printed Chinese | Printed English | Traditional Chinese | Ancient Text | Japanese | General Scenario | Pinyin | Rotation | Distortion | Artistic Text | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5807 | 0.5806 | 0.9013 | 0.8679 | 0.7472 | 0.6039 | 0.7372 | 0.5946 | 0.8384 | 0.7435 | 0.9314 | 0.6397 | 0.8401 |
Note: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications.
pip install -U paddleocr
pip install -U onnxruntime-gpu
paddleocr text_recognition -i ./demo.png --model_name PP-OCRv5_server_rec --engine onnxruntime
from paddleocr import TextRecognition
model = TextRecognition(
model_name="PP-OCRv5_server_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