en PP OCRv5 mobile rec

ProviderPaddlePaddle
Categoryimage-to-text
Licenseapache-2.0
Downloads34.2K
Stars1

Overview

The PP-OCRv5 mobile recognition model is a lightweight, high-efficiency text recognition engine optimized for edge deployment. Unlike heavy transformer-based models, this version focuses on balancing inference speed with character accuracy, specifically targeting mobile and embedded environments where latency and memory overhead are critical constraints. It is designed to integrate seamlessly into OCR pipelines to convert cropped text images into machine-readable strings. For developers, this means a significant reduction in CPU/GPU requirements without sacrificing the precision needed for real-world document digitizing or scene text recognition. It serves as a robust alternative to cloud-based OCR APIs, enabling fully offline, private, and fast text extraction.

Highlights

  • Optimized for low-latency mobile and edge device deployment
  • High-precision text recognition with minimal memory footprint
  • Apache-2.0 license for flexible commercial integration
  • Efficient offline processing reducing cloud API dependency

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("PaddlePaddle/en_PP-OCRv5_mobile_rec")
tokenizer = AutoTokenizer.from_pretrained("PaddlePaddle/en_PP-OCRv5_mobile_rec")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download PaddlePaddle/en_PP-OCRv5_mobile_rec

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download PaddlePaddle/en_PP-OCRv5_mobile_rec config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('PaddlePaddle/en_PP-OCRv5_mobile_rec')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/PaddlePaddle/en_PP-OCRv5_mobile_rec

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/PaddlePaddle/en_PP-OCRv5_mobile_rec

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('PaddlePaddle/en_PP-OCRv5_mobile_rec')
tokenizer = AutoTokenizer.from_pretrained('PaddlePaddle/en_PP-OCRv5_mobile_rec')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model PaddlePaddle/en_PP-OCRv5_mobile_rec

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model PaddlePaddle/en_PP-OCRv5_mobile_rec README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('PaddlePaddle/en_PP-OCRv5_mobile_rec')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/PaddlePaddle/en_PP-OCRv5_mobile_rec.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/PaddlePaddle/en_PP-OCRv5_mobile_rec.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'PaddlePaddle/en_PP-OCRv5_mobile_rec')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
library_name: PaddleOCR
language:

  • en

pipeline_tag: image-to-text
tags:
  • OCR

  • PaddlePaddle

  • PaddleOCR

  • textline_recognition

---

en_PP-OCRv5_mobile_rec

Introduction

en_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 English. The key accuracy metrics are as follow:

| Model | Accuracy (%) |
|-|-|
| en_PP-OCRv5_mobile_rec | 85.3|

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.

Quick Start

Installation

1. PaddlePaddle

Please refer to the following commands to install PaddlePaddle using pip:

bash
# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

for CUDA12.6

python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

for CPU

python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.

2. PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

bash
python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

bash
paddleocr text_recognition \
    --model_name en_PP-OCRv5_mobile_rec \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/QmaPtftqwOgCtx0AIvU2z.png

You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.

python
from paddleocr import TextRecognition
model = TextRecognition(model_name="en_PP-OCRv5_mobile_rec")
output = model.predict(input="QmaPtftqwOgCtx0AIvU2z.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")

After running, the obtained result is as follows:

json
{'res': {'input_path': '/root/.paddlex/predict_input/QmaPtftqwOgCtx0AIvU2z.png', 'page_index': None, 'rec_text': 'the number of model parameters and FLOPs get larger, it', 'rec_score': 0.993655264377594}}

The visualized image is as follows:

!image/jpeg

For details about usage command and descriptions of parameters, please refer to the Document.

Pipeline Usage

The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.

#### PP-OCRv5

The general OCR pipeline is used to solve text recognition tasks by extracting text information from images and outputting it in string format. And there are 5 modules in the pipeline:

  • Document Image Orientation Classification Module (Optional)

  • Text Image Unwarping Module (Optional)

  • Text Line Orientation Classification Module (Optional)

  • Text Detection Module

  • Text Recognition Module

Run a single command to quickly experience the OCR pipeline:

bash
paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/c3hSldnYVQXp48T5V0Ze4.png \
    --text_recognition_model_name en_PP-OCRv5_mobile_rec \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation True \
    --save_path ./output \
    --device gpu:0

Results are printed to the terminal:

json
{'res': {'input_path': '/root/.paddlex/predict_input/c3hSldnYVQXp48T5V0Ze4.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[252, 172],
        ...,
        [254, 241]],

...,


[[665, 566],
        ...,
        [663, 601]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0.0, 'return_word_box': False, 'rec_texts': ['The moon tells the sky', 'The sky tells the sea', 'The sea tells the tide', 'And the tide tells me', 'Lemn Sissay'], 'rec_scores': array([0.98405874, ..., 0.9837752 ]), 'rec_polys': array([[[252, 172],
        ...,
        [254, 241]],


...,


[[665, 566],
        ...,
        [663, 601]]], dtype=int16), 'rec_boxes': array([[252, ..., 241],
       ...,
       [663, ..., 612]], dtype=int16)}}

If save_path is specified, the visualization results will be saved under save_path. The visualization output is shown below:

!image/jpeg

The command-line method is for quick experience. For project integration, also only a few codes are needed as well:

python
from paddleocr import PaddleOCR

ocr = PaddleOCR(
    text_recognition_model_name="en_PP-OCRv5_mobile_rec",
    use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
    use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
    use_textline_orientation=True, # Use use_textline_orientation to enable/disable textline orientation classification model
    device="gpu:0", # Use device to specify GPU for model inference
)
result = ocr.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/6KQKOS42DKVEUnrticvhd.png")  
for res in result:  
    res.print()  
    res.save_to_img("output")  
    res.save_to_json("output")

The default model used in pipeline is PP-OCRv5_server_rec, so it is needed that specifing to en_PP-OCRv5_mobile_rec by argument text_recognition_model_name. And you can also use the local model file by argument text_recognition_model_dir. For details about usage command and descriptions of parameters, please refer to the Document.

Links

PaddleOCR Repo

PaddleOCR Documentation

View official source
Join our Telegram