PP OCRv4 Server Det

🚀 PP-OCRv4_server_det

PP-OCRv4_server_det 是 PP-OCRv4_det 系列模型之一，是由 PaddleOCR 团队开发的一组文本检测模型。这个服务端文本检测模型具有更高的准确率，适合部署在高性能服务器上。

✨ 主要特性

PP-OCRv4_server_det 模型的关键准确率指标如下：

📦 安装指南

1. 安装 PaddlePaddle

请参考以下命令，使用 pip 安装 PaddlePaddle：

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

# 适用于 CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

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

PaddlePaddle 安装详情请参考 PaddlePaddle 官方网站。

2. 安装 PaddleOCR

从 PyPI 安装最新版本的 PaddleOCR 推理包：

python -m pip install paddleocr

💻 使用示例

基础用法

你可以使用单个命令快速体验其功能：

paddleocr text_detection \
    --model_name PP-OCRv4_server_det \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png

你也可以将文本检测模块的模型推理集成到你的项目中。在运行以下代码之前，请将示例图像下载到本地。

from paddleocr import TextDetection
model = TextDetection(model_name="PP-OCRv4_server_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")

运行后，得到的结果如下：

{'res': {'input_path': '/root/.paddlex/predict_input/3ul2Rq4Sk5Cn-l69D695U.png', 'page_index': None, 'dt_polys': array([[[ 627, 1432],
        ...,
        [ 627, 1449]],

       ...,

       [[ 354,  106],
        ...,
        [ 354,  127]]], dtype=int16), 'dt_scores': [0.9421815230284514, 0.8528662776681952, ..., 0.8209321007152185]}}

可视化图像如下： 使用命令和参数说明的详细信息，请参考 文档。

高级用法

单个模型的能力是有限的，但由多个模型组成的管道可以提供更强的能力来解决现实场景中的难题。

PP-OCRv4

通用 OCR 管道用于通过从图像中提取文本信息并以文本形式输出，来解决文本识别任务。管道中有 5 个模块：

• 文档图像方向分类模块（可选）
• 文本图像矫正模块（可选）
• 文本行方向分类模块（可选）
• 文本检测模块
• 文本识别模块

运行单个命令快速体验 OCR 管道：

paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \
    --text_detection_model_name PP-OCRv4_server_det \
    --text_recognition_model_name PP-OCRv4_server_rec \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation False \
    --save_path ./output \
    --device gpu:0 

结果会打印到终端：

{'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([[[ 355,  109],
        ...,
        [ 355,  126]],

       ...,

       [[ 631, 1431],
        ...,
        [ 631, 1450]]], 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': ['AlgorithmsfortheMarkovEntropyDecomposition', 'AndrewJ.FerrisandDavidPoulin', 'DepartementdePhysique,UniversitedeSherbrooke，Quebec,JlK2Rl，Canada', '（Dated:October31，2018）', 'TheMarkoventropydecomposition', '(MED)is a recently-proposed, cluster-based simulation method for fi-', 'nite temperature quantum systems with arbitrary geometry. In this p', 'paper,wedetail numerical algorithms for', 'performing the required steps of the MED,principally solving a minimization problem with a', 'preconditioned', 'astea1ocrtratnrtpccc', "Newton's", 'algorithm,as well as how to extract global susceptibilities and thermal responses.', 'Wedemonstrate', 'the power of the method with the spin-l/2 XXZ model on the 2D squarelattice, including the extraction of', 'critical points and details of each phase. Although the method shares some qualitative similarities with exact-', 'diagonalization,we show the MED is both more accurate and significantly more flexible.', 'PACSnumbers:05.10.-a, 02.50.Ng,03.67.-a,74.40.Kb', 'I.INTRODUCTION', 'This approximation becomes exact in the case of a 1D', 'quan-', 'tum（orclassical)', 'Markov chain', '[10],and leads to an', 'expo-', 'Although', 'the', 'equations', 'governing', 'quantum many-body', 'nentialreduction', '1 of cost for exact entropy calculations when', 'systems are', 'simplet', 'writedown，findin', 'solutions', 'sforthe', 'the global density matrix is a higher-dimensional Markov net-', 'majority', 'of', 'systems', 'remains incredibly', 'difficult.', 'Modern', 'workstate', '[12,13].', 'physics finds itself in need of new', 'tools to compute the emer-', 'gentbehavior of large, many-body', 'ysystems.', 'related to the V-representibility problem. Given a set of lo-', 'Therehasbeen', 'greatvariety', 'r oftools developed to tackle', 'calbutoverlapping', 'reduced density', 'matrices{pi}，it is avery', 'many-bodyproblems', 's,butin', 'general,large', '2D', 'and3D', 'quan-', 'challenging', 'problemto determinei', 'ifthereexists', 'globalden-', 'tum', 'systems', 'remain', 'hard', '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', 'problemisQMA-hard', '(the', 'notusually expected.Direct numerical diagonalization canbe', 'quantum analogue of NP)', '[14，', '151.', 'and is hopelessly', 'diffi-', 'performedforrelatively', 'small', 'wstems', '-howevertheemer-', 'cultto', 'enforce.', 'Thus', 'the', 'second', 'pproximationemployed', 'gentbehaviorofas', 'ystem in thethermodynamiclimitmaybe', 'involves ignoringglobal consistency', 'with a', 'positive', 'opera-', 'difficult to extract, especially in s', 'ystemswithlarge', 'correlation', 'tor,while', 'requiring', 'localconsistenc', 'on any overlapping re-', 'lengths.MonteCarlo', 'approaches are technically', '/exact(upto', 'gionsbetween thep.Atthezero-temperaturelimit,theMED', 'sampling', 'error)，but', 'suffer', 'fromthe', 'so-called', 'ign', 'problem', 'approach becomes analogous to the', 'ariationalnth-orderre-', '111', 'for fermionic, frustrated, or dynamical problems. Thus', 'weare', 'duceddensity', 'matrix', 'approach,', 'where', 'positivity is enforced', 'limitedto', 'search for clever', ' approximations to solve the ma-', 'on all reduced density', 'matrices of size', '[16-18].', ' jority of many-body problems.', 'The MED approach is an extremely flexible cluster method,', 'Overthe', 'pastcentury', 'hundredsof such', 'approximations', 'applicable to both translationally', 'y invariant systems of any di-', 'havebeen', 'proposed,', ' and we will mention just a few notable', 'mension in the thermodynamic limit.', 'as well asfinite', 'ystems', 'examples', 'applicable', 'quantum lattice', 'odels.Mean-field', 'or systems', 'without translational invariance （e.g.', 'disordered', 'theory', 'is simple and frequently', 'arrives', 'at the correct', 'quali-', 'lattices,', 'or harmonically', 'trappedatomsin', 'optical lattices).', 'tative description,but', 'often fails', 'swhen', 'correlations', 'are im-', 'Thefreeenergygivenby', 'MEDis', 'guaranteed to lower', 'bound', 'portant. Density-matrix renormalisation', 'group (DMRG)[1]', 'the true free', 'energy', 'which in turn lower-bounds the', 'ground', 'is efficient and extremely', 'accurate at solving', '1D', 'problems,', 'stateenergy', '—thus', 'providing a natural complement to varia-', 'butthe computational cost', 'grows exponentially', 'ywithsystem', 'tionalapproacheswhich upper-boundthe', 'ground state energy.', 'size in two- or higher-dimensions', '[2,3].', 'Related tensor-', 'The ability to provide a rigorous', 's ground-stateenergywindow', 'networktechniquesdesigned for2D', 'ystemsarestill intheir', 'is apowerful validation tool, creating', 'averycompellingrea-', 'infancy [4-6]. Series-expansion methods', '7', 'canbe success-', 'son to use this approach.', 'ful, but may diverge', 'orotherwise converge', 'eslowly,obscuring', 'In this paper we paper we present a pedagogical introduc-', 'thestatein', '1certain', 'regimes. There exist', 'variety', 'of cluster-', 'tion toMED, includingnumericalimplementation issues and', 'based techniques', 'such as', 'dynamical-mean-field theory[8]', 'applications', 'to 2D quantum lattice', 'models in the thermody-', 'anddensity', '7-matrix', 'embedding[9].', 'namiclimit.', 'In Sec.II,we', 'give a brief derivation', 'ofthe', 'Herewe', 'discuss the so-called Markov entropy decompo-', 'Markov entropy decomposition.', 'SectionIlloutlinesarobust', 'sition (MED),recently', 'proposedby', 'Poulin&Hastings', '[10]', 'numerical strategy for', 'optimizing', 'the clusters that make up', '(andanalogous to a slightly', 'earlier', ' classical algorithm', '[11])', 'thedecomposition.', 'InSec.IVw', 'show how we can extend', 'This is', 'a self-consistent', 'cluster method for finite temperature', 'thesealgorithmsto', 'extract non-trivial', 'information,suchas', 'systems that takes advantage of an ', 'approximationofthe（von', 'specific heat and susceptibilities.We present an application of', 'Neumann)entropy.', 'In[10]，itwas', 'shown that the entropy', 'the method to the spin-1/2 XXZ model on a 2D squarelattice', 'per site can be rigorously upper bounded using only local in-', 'in Sec.V,describing how to characterize the phase diagram', 'formation—alocal.reduceddensity', 'matrix onN sites,say.', 'and determine critical points, before concluding in Sec.VI.'], 'rec_scores': array([0.98928159, ..., 0.98077077]), 'rec_polys': array([[[ 355,  109],
        ...,
        [ 355,  126]],

       ...,

       [[ 631, 1431],
        ...,
        [ 631, 1450]]], dtype=int16), 'rec_boxes': array([[ 355, ...,  126],
       ...,
       [ 631, ..., 1450]], dtype=int16)}}

如果指定了 save_path，可视化结果将保存到 save_path 下。可视化输出如下： 命令行方法用于快速体验。对于项目集成，也只需要几行代码：

from paddleocr import PaddleOCR  

ocr = PaddleOCR(
    text_detection_model_name="PP-OCRv4_server_det",
    text_recognition_model_name="PP-OCRv4_server_rec",
    use_doc_orientation_classify=False, # 通过此参数禁用文档方向分类模型
    use_doc_unwarping=False, # 通过此参数禁用文本图像矫正模型
    use_textline_orientation=False, # 通过此参数禁用文本行方向分类模型
)
result = ocr.predict("./general_ocr.png")  
for res in result:  
    res.print()  
    res.save_to_img("output")  
    res.save_to_json("output")

使用命令和参数说明的详细信息，请参考 文档。

📚 详细文档

• PaddleOCR 仓库
• PaddleOCR 文档

📄 许可证

本项目采用 Apache-2.0 许可证。