Quick Start¶

Note: * The All-in-One development tool PaddleX, based on the advanced technology of PaddleOCR, supports all-in-one development capabilities in the OCR field. Through all-in-one development, simple and efficient model use, combination, and customization can be achieved. * PaddleX is committed to achieving pipeline-level model training, inference, and deployment. A model pipeline refers to a series of predefined development processes for specific AI tasks, including combinations of single models (single-function modules) that can independently complete a type of task. This document provides quick inference usage of the OCR-related pipelines. For quick usage of single-function modules and more features, please refer to the relevant sections in All-in-One Development of PaddleOCR.

🛠️ Installation¶

❗Before installing PaddleX, please ensure you have a basic Python environment (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).

Installing PaddlePaddle

# cpu
python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/

❗For more PaddlePaddle versions, please refer to the PaddlePaddle official website. * Installing PaddleX

pip install paddlex==3.0.0b2

❗For more installation methods, refer to the PaddleX Installation Guide.

📝 Python Script Usage¶

A few lines of code can complete the quick inference of the pipeline, the unified Python script format is as follows:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline=[Pipeline Name])
output = pipeline.predict([Input Image Name])
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")

The following steps are executed:

create_pipeline() instantiates the pipeline object
Passes the image and calls the predict method of the pipeline object for inference prediction
Processes the prediction results

For other pipelines in Python scripts, just adjust the pipeline parameter of the create_pipeline() method to the corresponding name of the pipeline. Below is a list of each pipeline's corresponding parameter name and detailed usage explanation:

👉 More Python Script Usages for Pipelines

Pipeline Name	Corresponding Parameter	Detailed Explanation
PP-ChatOCRv3-doc	`PP-ChatOCRv3-doc`	Python Script Usage for PP-ChatOCRv3-doc Pipeline
OCR	`OCR`	Python Script Usage for General OCR Pipeline
Table Recognition	`table_recognition`	Python Script Usage for Table Recognition Pipeline
Layout Parsing	`layout_parsing`	Python Script Usage for Layout Parsing Pipeline
Formula Recognition	`formula_recognition`	Python Script Usage for Formula Recognition Pipeline
Seal Recognition	`seal_recognition`	Python Script Usage for Formula Recognition Pipeline

💻 Command Line Usage¶

You can quickly experience the pipeline effect with a single command. The unified command line format is:

paddlex --pipeline [pipeline_name] --input [input_image] --device [device]

You only need to specify three parameters: * pipeline: the name of the pipeline * input: the local path or URL of the input file (e.g., image) to be processed * device: the GPU index to use (e.g., gpu:0 means using the first GPU), or you can choose to use the CPU (cpu)

For example, for the General OCR pipeline:

paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0

The result of the operation is as follows:

{'input_path': '/root/.paddlex/predict_input/general_ocr_002.png', 'dt_polys': [[[5, 12], [88, 10], [88, 29], [5, 31]], [[208, 14], [249, 14], [249, 22], [208, 22]], [[695, 15], [824, 15], [824, 60], [695, 60]], [[158, 27], [355, 23], [356, 70], [159, 73]], [[421, 25], [659, 19], [660, 59], [422, 64]], [[337, 104], [460, 102], [460, 127], [337, 129]], [[486, 103], [650, 100], [650, 125], [486, 128]], [[675, 98], [835, 94], [835, 119], [675, 124]], [[64, 114], [192, 110], [192, 131], [64, 134]], [[210, 108], [318, 106], [318, 128], [210, 130]], [[82, 140], [214, 138], [214, 163], [82, 165]], [[226, 136], [328, 136], [328, 161], [226, 161]], [[404, 134], [432, 134], [432, 161], [404, 161]], [[509, 131], [570, 131], [570, 158], [509, 158]], [[730, 138], [771, 138], [771, 154], [730, 154]], [[806, 136], [817, 136], [817, 146], [806, 146]], [[342, 175], [470, 173], [470, 197], [342, 199]], [[486, 173], [616, 171], [616, 196], [486, 198]], [[677, 169], [813, 166], [813, 191], [677, 194]], [[65, 181], [170, 177], [171, 202], [66, 205]], [[96, 208], [171, 205], [172, 230], [97, 232]], [[336, 220], [476, 215], [476, 237], [336, 242]], [[507, 217], [554, 217], [554, 236], [507, 236]], [[87, 229], [204, 227], [204, 251], [87, 254]], [[344, 240], [483, 236], [483, 258], [344, 262]], [[66, 252], [174, 249], [174, 271], [66, 273]], [[75, 279], [264, 272], [265, 297], [76, 303]], [[459, 297], [581, 295], [581, 320], [459, 322]], [[101, 314], [210, 311], [210, 337], [101, 339]], [[68, 344], [165, 340], [166, 365], [69, 368]], [[345, 350], [662, 346], [662, 368], [345, 371]], [[100, 459], [832, 444], [832, 465], [100, 480]]], 'dt_scores': [0.8183103704439653, 0.7609575621092027, 0.8662357274035412, 0.8619508290334809, 0.8495855993183273, 0.8676840017933314, 0.8807986687956436, 0.822308525056085, 0.8686617037621976, 0.8279022169854463, 0.952332847006758, 0.8742692553015098, 0.8477013022907575, 0.8528771493227294, 0.7622965906848765, 0.8492388224448705, 0.8344203789965632, 0.8078477124353284, 0.6300434587457232, 0.8359967356998494, 0.7618617265751318, 0.9481573079350023, 0.8712182945408912, 0.837416955846334, 0.8292475059403851, 0.7860382856406026, 0.7350527486717117, 0.8701022267947695, 0.87172526903969, 0.8779847108088126, 0.7020437651809734, 0.6611684983372949], 'rec_text': ['www.997', '151', 'PASS', '登机牌', 'BOARDING', '舱位 CLASS', '序号SERIALNO.', '座位号SEATNO', '航班 FLIGHT', '日期DATE', 'MU 2379', '03DEC', 'W', '035', 'F', '1', '始发地FROM', '登机口 GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO.', '姓名NAME', 'ZHANGQIWEI', '票号TKTNO.', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE1OMINUTESBEFOREDEPARTURETIME'], 'rec_score': [0.9617719054222107, 0.4199012815952301, 0.9652514457702637, 0.9978302121162415, 0.9853208661079407, 0.9445787072181702, 0.9714463949203491, 0.9841841459274292, 0.9564052224159241, 0.9959094524383545, 0.9386572241783142, 0.9825271368026733, 0.9356589317321777, 0.9985442161560059, 0.3965512812137604, 0.15236201882362366, 0.9976775050163269, 0.9547433257102966, 0.9974752068519592, 0.9646636843681335, 0.9907559156417847, 0.9895358681678772, 0.9374122023582458, 0.9909093379974365, 0.9796401262283325, 0.9899340271949768, 0.992210865020752, 0.9478569626808167, 0.9982215762138367, 0.9924325942993164, 0.9941263794898987, 0.96443772315979]}
......

The visualization result is as follows:

alt text

For command line usage of other pipelines, simply adjust the pipeline parameter to the name of the respective pipeline. Below are the commands corresponding to each pipeline:

👉 More Command Line Usages for Pipelines

Pipeline Name	Command
Table Recognition	`paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0`
Layout Parsing	`paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0`
Formula Recognition	`paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0`
Seal Recognition	`paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0`