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Inference

We provide an easy-to-use API for the demo and application purpose in ocr.py script.

The API can be called through command line (CL) or by calling it from another python script. It exposes all the models in MMOCR to API as individual modules that can be called and chained together.

Warning

This interface is being refactored is much likely to be changed in the upcoming release.

Example 1: Text Detection

Instruction: Perform detection inference on an image with the TextSnake recognition model, export the result in a json file (default) and save the visualization file.

  • CL interface:

python mmocr/ocr.py demo/demo_text_det.jpg --det TextSnake --img-out-dir demo/
  • Python interface:

from mmocr.ocr import MMOCR

# Load models into memory
ocr = MMOCR(det='TextSnake')

# Inference
results = ocr.readtext('demo/demo_text_det.jpg', img_out_dir='demo/')

Example 2: Text Detection + Recognition

Instruction: Perform ocr (det + recog) inference on the demo/demo_text_det.jpg image with the DB_r18 detection model and CRNN recognition model, print the result in the terminal and show the visualization.

  • CL interface:

python mmocr/ocr.py --det DB_r18 --recog CRNN demo/demo_text_ocr.jpg --print-result --show

Note

When calling the script from the command line, the script assumes configs are saved in the configs/ folder. User can customize the directory by specifying the value of config_dir.

  • Python interface:

from mmocr.ocr import MMOCR

# Load models into memory
ocr = MMOCR(det='DB_r18', recog='CRNN')

# Inference
results = ocr.readtext('demo/demo_text_ocr.jpg', print_result=True, show=True)

Example 3: Text Detection + Recognition + Key Information Extraction

Instruction: Perform end-to-end ocr (det + recog) inference first with DB_r18 detection model and CRNN recognition model, then run KIE inference with SDMGR model on the ocr result and show the visualization.

  • CL interface:

python mmocr/ocr.py demo/demo_kie.jpeg  --det DB_r18 --recog CRNN --kie SDMGR --print-result --show

Note

Note: When calling the script from the command line, the script assumes configs are saved in the configs/ folder. User can customize the directory by specifying the value of config_dir.

  • Python interface:

from mmocr.ocr import MMOCR

# Load models into memory
ocr = MMOCR(det='DB_r18', recog='CRNN', kie='SDMGR')

# Inference
results = ocr.readtext('demo/demo_kie.jpeg', print_result=True, show=True)

API Arguments

The API has an extensive list of arguments that you can use. The following tables are for the python interface.

MMOCR():

Arguments Type Default Description
det see models None Text detection algorithm
recog see models None Text recognition algorithm
kie [1] see models None Key information extraction algorithm
config_dir str configs/ Path to the config directory where all the config files are located
det_config str None Path to the custom config file of the selected det model
det_ckpt str None Path to the custom checkpoint file of the selected det model
recog_config str None Path to the custom config file of the selected recog model
recog_ckpt str None Path to the custom checkpoint file of the selected recog model
kie_config str None Path to the custom config file of the selected kie model
kie_ckpt str None Path to the custom checkpoint file of the selected kie model
device str None Device used for inference, accepting all allowed strings by torch.device. E.g., 'cuda:0' or 'cpu'.

[1]: kie is only effective when both text detection and recognition models are specified.

Note

User can use default pretrained models by specifying det and/or recog, which is equivalent to specifying their corresponding *_config and *_ckpt. However, manually specifying *_config and *_ckpt will always override values set by det and/or recog. Similar rules also apply to kie, kie_config and kie_ckpt.

readtext()

Arguments Type Default Description
img str/list/tuple/np.array required img, folder path, np array or list/tuple (with img paths or np arrays)
img_out_dir str None Output directory of images.
show bool False Whether to show the result visualization on screen
print_result bool False Whether to show the result for each image

All arguments are the same for the cli, all you need to do is add 2 hyphens at the beginning of the argument and replace underscores by hyphens. (Example: img_out_dir becomes --img-out-dir)

For bool type arguments, putting the argument in the command stores it as true. (Example: python mmocr/demo/ocr.py --det DB_r18 demo/demo_text_det.jpg --print_result means that print_result is set to True)

Models

Text detection:

Name Reference
DB_r18 link
DB_r50 link
DBPP_r50 link
DRRG link
FCE_IC15 link
FCE_CTW_DCNv2 link
MaskRCNN_CTW link
MaskRCNN_IC15 link
PANet_CTW link
PANet_IC15 link
PS_CTW link
PS_IC15 link
TextSnake link

Text recognition:

Name Reference
ABINet link
ABINet_Vision link
ASTER link
CRNN link
MASTER link
NRTR_1/16-1/8 link
NRTR_1/8-1/4 link
RobustScanner link
SAR link
SATRN link
SATRN_sm link

Key information extraction:

Name Reference
SDMGR link

Additional info

  • To perform det + recog inference (end2end ocr), both the det and recog arguments must be defined.

  • To perform only detection set the recog argument to None.

  • To perform only recognition set the det argument to None.

If you have any suggestions for new features, feel free to open a thread or even PR :)

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