Model reference · open weights

PP-OCRv5_mobile_rec_safetensors

LLMs PaddlePaddle Image→text 1 build Open weights 756 dl/mo

PP-OCRv5_mobile_rec_safetensors is an open-weight language model from Baidu. PP-OCRv5_mobile_rec_safetensors (FP32) weighs 16 MB; the smallest configuration that runs it is RTX 3060 12 GB.

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

  • PP-OCRv5_mobile_rec_safetensors is an 8M parameter image-to-text model developed by Baidu for text line recognition.
  • It supports English and Chinese, as well as Traditional Chinese and Japanese, and is designed to handle complex scenarios including handwriting, vertical text, and rare characters.
  • The model is licensed under apache-2.0.

Summary of the PaddlePaddle/PP-OCRv5_mobile_rec_safetensors model card, 2026-10-01

What it is

Released byBaidu
Released2026-03-18
Parameters8M
VRAM16 MB for the weights

What it runs on

Memory and cards for PP-OCRv5_mobile_rec_safetensors (FP32)

16 MBweights, file size
566 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What Baidu says about PP-OCRv5_mobile_rec_safetensors

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

import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForTextRecognition

model_path = "PaddlePaddle/PP-OCRv5_mobile_rec_safetensors"
model = AutoModelForTextRecognition.from_pretrained(model_path, device_map="auto")
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_rec_001.png", stream=True).raw).convert("RGB")
inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

results = image_processor.post_process_text_recognition(outputs)

for result in results:
    print(result)

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

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