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--- |
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license: apache-2.0 |
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library_name: PaddleOCR |
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language: |
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- en |
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- zh |
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pipeline_tag: image-to-text |
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tags: |
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- OCR |
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- PaddlePaddle |
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- PaddleOCR |
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- textline_detection |
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--- |
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# PP-OCRv3_mobile_det |
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## Introduction |
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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. |
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## Quick Start |
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### Installation |
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1. PaddlePaddle |
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Please refer to the following commands to install PaddlePaddle using pip: |
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```bash |
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# for CUDA11.8 |
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python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ |
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# for CUDA12.6 |
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python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ |
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# for CPU |
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python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ |
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``` |
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For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). |
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2. PaddleOCR |
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Install the latest version of the PaddleOCR inference package from PyPI: |
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```bash |
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python -m pip install paddleocr |
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``` |
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### Model Usage |
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You can quickly experience the functionality with a single command: |
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```bash |
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paddleocr text_detection \ |
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--model_name PP-OCRv3_mobile_det \ |
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-i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png |
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``` |
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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. |
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```python |
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from paddleocr import TextDetection |
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model = TextDetection(model_name="PP-OCRv3_mobile_det") |
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output = model.predict(input="3ul2Rq4Sk5Cn-l69D695U.png", batch_size=1) |
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for res in output: |
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res.print() |
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res.save_to_img(save_path="./output/") |
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res.save_to_json(save_path="./output/res.json") |
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``` |
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After running, the obtained result is as follows: |
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```json |
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{'res': {'input_path': '/root/.paddlex/predict_input/3ul2Rq4Sk5Cn-l69D695U.png', 'page_index': None, 'dt_polys': array([[[ 637, 1429], |
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..., |
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[ 634, 1450]], |
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..., |
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[[ 356, 106], |
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..., |
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[ 356, 127]]], dtype=int16), 'dt_scores': [0.8440782190003071, 0.7211973560197601, ..., 0.9473868156887905]}} |
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``` |
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The visualized image is as follows: |
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For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_detection.html#iii-quick-start). |
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### Pipeline Usage |
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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. |
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#### PP-OCRv3 |
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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: |
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* Document Image Orientation Classification Module (Optional) |
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* Text Image Unwarping Module (Optional) |
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* Text Line Orientation Classification Module (Optional) |
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* Text Detection Module |
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* Text Recognition Module |
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Run a single command to quickly experience the OCR pipeline: |
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```bash |
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paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \ |
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--text_detection_model_name PP-OCRv3_mobile_det \ |
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--text_recognition_model_name PP-OCRv3_mobile_rec \ |
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--use_doc_orientation_classify False \ |
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--use_doc_unwarping False \ |
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--use_textline_orientation False \ |
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--save_path ./output \ |
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--device gpu:0 |
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``` |
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Results are printed to the terminal: |
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```json |
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{'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], |
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..., |
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[ 354, 127]], |
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..., |
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[[ 633, 1433], |
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..., |
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[ 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 has been a great variety of tools developed to tackle', 'cal but overlapping reduced density matrices fp:f, it is a very', 'many-bodyproblems,butingeneral,large2Dand3Dquan-', 'challengingproblem to determine if there exists aglobal den-', 'tum systems remain hard to deal with.Most systems are', 'sity operator which is positive semi-definite and whose partial', 'thought to be non-integrable,so exact analytic solutions are', 'trace agrees with each p. This problem is QMA-hard (the', 'notusuallyexpected.Directnumerical diagonalizationcanbe', 'quantumanalogue of NP)[14,15],and is hopelessly diffi-', 'performed for relatively small systemshowever the emer-', 'culttoenforce.Thus,thesecondapproximationemployed', 'gentbehavior of a system in thethermodynamic limitmaybe', 'involves ignoringglobal consistency withapositive opera-', 'difficult to extract, especially in systems with large correlation', 'tor,whilerequiringlocalconsistencyonanyoverlappingre-', 'lengths.MonteCarlo approaches aretechnically exact (up to', 'gions between the pi. At the zero-temperature limit, the MED', '', 'sampling error),but sufferfrom the so-called sign problem', 'approach becomes analogous tothe variational nth-order re-', '一', 'forfermionic,frustrated,or dynamicalproblems.Thus we are', 'duced density matrix approach, where positivity is enforced', '', 'limited to search for clever approximations to solve the ma-', 'onallreduceddensitymatricesofsizen[16-18].', 'jorityofmany-bodyproblems.', 'The MED approachis an extremely flexible cluster method,', 'Over the past century,hundreds of such approximations', 'applicabletobothtranslationally invariant systems of anydi-', 'have been proposed, and we will mention just a few notable', 'mensioninthethermodynamiclimit,aswell asfinite systems', '1', 'examples applicable to quantumlattice models.Mean-field', 'or systems without translational invariance (e.g. disordered', 'theory is simple and frequently arrives at the correct quali-', 'lattices,orharmonicallytrapped atoms in optical lattices)', '11', 'tativedescription,butoftenfails when correlations areim-', 'Thefree energy given byMED is guaranteed to lowerbound', 'portant.Density-matrix renormalisation group (DMRG) [1]', 'the true free energy, which in turn lower-bounds the ground', '[ :A!', 'is efficient and extremely accurate at solving 1D problems', 'state energy-thus providing a natural complement to varia-', 'but the computational cost grows exponentially with system', 'tional approaches which upper-bound the ground state energy.', '!XIe', 'size in two- or higher-dimensions [2, 3].Related tensor-', 'The ability to provide a rigorous ground-state energy window', 'networktechniquesdesignedfor2Dsystemsarestillintheir', 'is a powerful validation tool, creating a very compellingrea-', 'infancy[4-6].Series-expansionmethods[7]canbe success-', 'son to use this approach.', 'ful, but may diverge or otherwise converge slowly, obscuring', 'In this paper we paper we present a pedagogical introduc-', 'the state in certain regimes. There exist a variety of cluster-', 'tion to MED, including numerical implementation issues and', 'based techniques, such as dynamical-mean-field theory[8]', 'applicationsto 2D quantumlatticemodels in thethermody-', 'and density-matrix embedding [9].', 'namiclimit.In Sec.II,we give a brief derivation of the', 'Herewediscusstheso-calledMarkoventropydecompo-', 'Markov entropydecomposition.SectionIIIoutlinesarobust', 'sition (MED),recently proposed by Poulin & Hastings [1O]', 'numerical strategy for optimizing the clusters that make up', '(andanalogoustoaslightlyearlierclassicalalgorithm[11)).', 'thedecomposition.InSec.IVweshowhowwecanextend', 'This is a self-consistent cluster method for finitetemperature', 'these algorithms toextractnon-trivial information,such as', 'systemsthattakesadvantageofanapproximationofthe(von', 'specific heat and susceptibilities. We present an application of', 'Neumann) entropy. In [1o], it was shown that the entropy', 'the method to the spin-1/2 XXZ model on a 2D square lattice', 'persitecanberigorouslyupperboundedusingonlylocalin-', 'inSec.V,describinghowtocharacterizethephasediagram', 'formationa local,reduced densitymatrix onN sites,say.', 'anddeterminecriticalpoints,beforeconcludinginSec.Vl'], 'rec_scores': array([0.92904288, ..., 0.92923349]), 'rec_polys': array([[[ 354, 106], |
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..., |
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[ 354, 127]], |
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..., |
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[[ 633, 1433], |
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..., |
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[ 633, 1449]]], dtype=int16), 'rec_boxes': array([[ 354, ..., 128], |
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..., |
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[ 633, ..., 1449]], dtype=int16)}} |
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``` |
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If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: |
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The command-line method is for quick experience. For project integration, also only a few codes are needed as well: |
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```python |
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from paddleocr import PaddleOCR |
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ocr = PaddleOCR( |
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text_detection_model_name="PP-OCRv3_mobile_det", |
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text_recognition_model_name="PP-OCRv3_mobile_rec", |
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use_doc_orientation_classify=False, # Disables document orientation classification model via this parameter |
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use_doc_unwarping=False, # Disables text image rectification model via this parameter |
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use_textline_orientation=False, # Disables text line orientation classification model via this parameter |
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) |
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result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png") |
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for res in result: |
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res.print() |
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res.save_to_img("output") |
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res.save_to_json("output") |
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``` |
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For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). |
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## Links |
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[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) |
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[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) |
