mmocr/docs/en/user_guides/inference.md

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.

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

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: 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

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
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 :)