readme_en.md

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PaddleOCR provides 2 service deployment methods:

• Based on PaddleHub Serving: Code path is "./deploy/hubserving". Please follow this tutorial.
• Based on PaddleServing: Code path is "./deploy/pdserving". Please refer to the tutorial for usage.

Service deployment based on PaddleHub Serving

The hubserving service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Please select the corresponding service package to install and start service according to your needs. The directory is as follows:

deploy/hubserving/
  └─  ocr_det     detection module service package
  └─  ocr_rec     recognition module service package
  └─  ocr_system  two-stage series connection service package

Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:

deploy/hubserving/ocr_system/
  └─  __init__.py    Empty file, required
  └─  config.json    Configuration file, optional, passed in as a parameter when using configuration to start the service
  └─  module.py      Main module file, required, contains the complete logic of the service
  └─  params.py      Parameter file, required, including parameters such as model path, pre- and post-processing parameters

Quick start service

The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path.

1. Prepare the environment

# Install paddlehub  
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple

2. Download inference model

Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default model path is:

detection model: ./inference/ch_ppocr_mobile_v1.1_det_infer/
recognition model: ./inference/ch_ppocr_mobile_v1.1_rec_infer/
text direction classifier: ./inference/ch_ppocr_mobile_v1.1_cls_infer/

The model path can be found and modified in params.py. More models provided by PaddleOCR can be obtained from the model library. You can also use models trained by yourself.

3. Install Service Module

PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.

• On Linux platform, the examples are as follows.

# Install the detection service module:
hub install deploy/hubserving/ocr_det/

# Or, install the recognition service module:
hub install deploy/hubserving/ocr_rec/

# Or, install the 2-stage series service module:
hub install deploy/hubserving/ocr_system/

• On Windows platform, the examples are as follows.

# Install the detection service module:
hub install deploy\hubserving\ocr_det\

# Or, install the recognition service module:
hub install deploy\hubserving\ocr_rec\

# Or, install the 2-stage series service module:
hub install deploy\hubserving\ocr_system\

4. Start service

Way 1. Start with command line parameters (CPU only)

start command：

$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
                    --port XXXX \
                    --use_multiprocess \
                    --workers \

parameters：

parameters	usage
--modules/-m	PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs When Version is not specified, the latest version is selected by default
--port/-p	Service port, default is 8866
--use_multiprocess	Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines Windows operating system only supports single-process mode
--workers	The number of concurrent tasks specified in concurrent mode, the default is 2*cpu_count-1, where cpu_count is the number of CPU cores

For example, start the 2-stage series service:

hub serving start -m ocr_system

This completes the deployment of a service API, using the default port number 8866.

Way 2. Start with configuration file（CPU、GPU）

start command：

hub serving start --config/-c config.json

Wherein, the format of config.json is as follows:

{
    "modules_info": {
        "ocr_system": {
            "init_args": {
                "version": "1.0.0",
                "use_gpu": true
            },
            "predict_args": {
            }
        }
    },
    "port": 8868,
    "use_multiprocess": false,
    "workers": 2
}

• The configurable parameters in init_args are consistent with the _initialize function interface in module.py. Among them, when use_gpu is true, it means that the GPU is used to start the service.
• The configurable parameters in predict_args are consistent with the predict function interface in module.py.

Note:

• When using the configuration file to start the service, other parameters will be ignored.
• If you use GPU prediction (that is, use_gpu is set to true), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as: export CUDA_VISIBLE_DEVICES=0, otherwise you do not need to set it.
• use_gpu and use_multiprocess cannot be true at the same time.

For example, use GPU card No. 3 to start the 2-stage series service:

export CUDA_VISIBLE_DEVICES=3
hub serving start -c deploy/hubserving/ocr_system/config.json

Send prediction requests

After the service starts, you can use the following command to send a prediction request to obtain the prediction result:

python tools/test_hubserving.py server_url image_path

Two parameters need to be passed to the script:

• server_url：service address，format of which is http://[ip_address]:[port]/predict/[module_name]
  For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective server_url would be:
  http://127.0.0.1:8866/predict/ocr_det
http://127.0.0.1:8867/predict/ocr_rec
http://127.0.0.1:8868/predict/ocr_system
• image_path：Test image path, can be a single image path or an image directory path

Eg.

python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/

Returned result format

The returned result is a list. Each item in the list is a dict. The dict may contain three fields. The information is as follows:

field name	data type	description
text	str	text content
confidence	float	text recognition confidence
text_region	list	text location coordinates

The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain text_region. The details are as follows:

field name/module name	ocr_det	ocr_rec	ocr_system
text		✔	✔
confidence		✔	✔
text_region	✔		✔

Note： If you need to add, delete or modify the returned fields, you can modify the file module.py of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section.

User defined service module modification

If you need to modify the service logic, the following steps are generally required (take the modification of ocr_system for example):

• 1. Stop service

hub serving stop --port/-p XXXX

• 2. Modify the code in the corresponding files, like module.py and params.py, according to the actual needs.
     For example, if you need to replace the model used by the deployed service, you need to modify model path parameters det_model_dir and rec_model_dir in params.py. If you want to turn off the text direction classifier, set the parameter use_angle_cls to False. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run module.py directly for debugging after modification before starting the service test.
• 3. Uninstall old service module

hub uninstall ocr_system

• 4. Install modified service module

hub install deploy/hubserving/ocr_system/

• 5. Restart service

hub serving start -m ocr_system