PP-OCRv3_mobile_det

Introduction

PP-OCRv3_mobile_det is one of the PP-OCRv3_det series models, a set of text detection models developed by the PaddleOCR team. This mobile-optimized text detection model offers higher efficiency, making it ideal for deployment on edge devices.

Quick Start

Installation

1. PaddlePaddle

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

# 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:

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

paddleocr text_detection \
    --model_name PP-OCRv3_mobile_det \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png

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

from paddleocr import TextDetection
model = TextDetection(model_name="PP-OCRv3_mobile_det")
output = model.predict(input="3ul2Rq4Sk5Cn-l69D695U.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:

{'res': {'input_path': '/root/.paddlex/predict_input/3ul2Rq4Sk5Cn-l69D695U.png', 'page_index': None, 'dt_polys': array([[[ 637, 1429],
        ...,
        [ 634, 1450]],

       ...,

       [[ 356,  106],
        ...,
        [ 356,  127]]], dtype=int16), 'dt_scores': [0.8440782190003071, 0.7211973560197601, ..., 0.9473868156887905]}}

The visualized image is as follows:

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-OCRv3

The general OCR pipeline is used to solve text recognition tasks by extracting text information from images and outputting it in text form. 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:

paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \
    --text_detection_model_name PP-OCRv3_mobile_det \
    --text_recognition_model_name PP-OCRv3_mobile_rec \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation False \
    --save_path ./output \
    --device gpu:0

Results are printed to the terminal:

{'res': {'input_path': '/root/.paddlex/predict_input/3ul2Rq4Sk5Cn-l69D695U.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([[[ 354,  106],
        ...,
        [ 354,  127]],

       ...,

       [[ 633, 1433],
        ...,
        [ 633, 1449]]], 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, 'rec_texts': ['Algorithms for the Markov Entropy Decomposition', 'Andrew J.Ferris and David Poulin', 'Departement de Physique, Universite de Sherbrooke,Quebec, JIK 2RI, Canada', '(Dated: October 31,2018)', 'The Markov entropy decomposition (MED)is a recently-proposed, cluster-based simulation method for fi-', 'nite temperature quantum systems with arbitrary geometry. In this paper, we detail numerical algorithms for', 'performing the required steps of the MED,principally solving aminimization problem with a preconditioned', '09', "Newton's algorithm, aswell ashowtoextractglobal susceptibilities and thermal responses.Wedemonstrate", 'thepower of the method withthe spin-1/2XXZmodel on the 2D square lattice, including the extraction of', 'criticalpointsanddetailsofeachphase.Althoughthemethodsharessomequalitativesimilaritieswithexact-', 'diagonalization, we show theMEDisbothmore accurate and significantlymoreflexible.', 'PACS numbers: 05.10.a, 02.50.Ng, 03.67.a, 74.40.Kb', 'I.INTRODUCTION', 'This approximation becomes exact in the case of a1D quan-', 'tum (or classical) Markov chain [1O], and leads to an expo-', '[', 'Although the equations governing quantum many-body', 'nential reduction of costforexactentropy calculationswhen', 'systemsare simpleto write down,finding solutions for the', 'theglobaldensitymatrixis ahigher-dimensional Markovnet-', 'majority of systems remains incrediblydifficult.Modern', 'work state[12, 13].', 'physics finds itself in need of new tools to compute the emer-', 'The second approximation used in theMED approach is', 'gent behavior of large, many-body systems.', 'related to the N-representibilityproblem.Givena set of lo-', 'There h