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Demo

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.

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/utils/ocr.py demo/demo_text_det.jpg --output demo/det_out.jpg --det TextSnake --recog None --export demo/
  • Python interface:
from mmocr.utils.ocr import MMOCR

# Load models into memory
ocr = MMOCR(det='TextSnake', recog=None)

# Inference
results = ocr.readtext('demo/demo_text_det.jpg', output='demo/det_out.jpg', export='demo/')

Example 2: Text Recognition



Instruction: Perform batched recognition inference on a folder with hundreds of image with the CRNN_TPS recognition model and save the visualization results in another folder. Batch size is set to 10 to prevent out of memory CUDA runtime errors.

  • CL interface:
python mmocr/utils/ocr.py %INPUT_FOLDER_PATH% --det None --recog CRNN_TPS --batch-mode --single-batch-size 10 --output %OUPUT_FOLDER_PATH%
  • Python interface:
from mmocr.utils.ocr import MMOCR

# Load models into memory
ocr = MMOCR(det=None, recog='CRNN_TPS')

# Inference
results = ocr.readtext(%INPUT_FOLDER_PATH%, output = %OUTPUT_FOLDER_PATH%, batch_mode=True, single_batch_size = 10)

Example 3: Text Detection + Recognition



Instruction: Perform ocr (det + recog) inference on the demo/demo_text_det.jpg image with the PANet_IC15 (default) detection model and SAR (default) recognition model, print the result in the terminal and show the visualization.

  • CL interface:
python mmocr/utils/ocr.py demo/demo_text_ocr.jpg --print-result --imshow

:::{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.utils.ocr import MMOCR

# Load models into memory
ocr = MMOCR()

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

Example 4: Text Detection + Recognition + Key Information Extraction



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

  • CL interface:
python mmocr/utils/ocr.py demo/demo_kie.jpeg  --det PS_CTW --recog SAR --kie SDMGR --print-result --imshow

:::{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.utils.ocr import MMOCR

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

# Inference
results = ocr.readtext('demo/demo_kie.jpeg', print_result=True, imshow=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 PANet_IC15 Text detection algorithm
recog see models SAR 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)
output str None Output result visualization - img path or folder path
batch_mode bool False Whether use batch mode for inference [1]
det_batch_size int 0 Batch size for text detection (0 for max size)
recog_batch_size int 0 Batch size for text recognition (0 for max size)
single_batch_size int 0 Batch size for only detection or recognition
export str None Folder where the results of each image are exported
export_format str json Format of the exported result file(s)
details bool False Whether include the text boxes coordinates and confidence values
imshow bool False Whether to show the result visualization on screen
print_result bool False Whether to show the result for each image
merge bool False Whether to merge neighboring boxes [2]
merge_xdist float 20 The maximum x-axis distance to merge boxes

[1]: Make sure that the model is compatible with batch mode.

[2]: Only effective when the script is running in det + recog mode.

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: det_batch_size becomes --det-batch-size)

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

Models

Text detection:

Name Reference batch_mode inference support
DB_r18 link :x:
DB_r50 link :x:
DRRG link :x:
FCE_IC15 link :x:
FCE_CTW_DCNv2 link :x:
MaskRCNN_CTW link :x:
MaskRCNN_IC15 link :x:
MaskRCNN_IC17 link :x:
PANet_CTW link :heavy_check_mark:
PANet_IC15 link :heavy_check_mark:
PS_CTW link :x:
PS_IC15 link :x:
TextSnake link :heavy_check_mark:

Text recognition:

Name Reference batch_mode inference support
ABINet link :heavy_check_mark:
CRNN link :x:
SAR link :heavy_check_mark:
SAR_CN * link :heavy_check_mark:
NRTR_1/16-1/8 link :heavy_check_mark:
NRTR_1/8-1/4 link :heavy_check_mark:
RobustScanner link :heavy_check_mark:
SATRN link :heavy_check_mark:
SATRN_sm link :heavy_check_mark:
SEG link :x:
CRNN_TPS link :heavy_check_mark:

:::{warning}

SAR_CN is the only model that supports Chinese character recognition and it requires a Chinese dictionary. Please download the dictionary from here for a successful run.

:::

Key information extraction:

Name Reference batch_mode support
SDMGR link :heavy_check_mark:

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.
  • details argument only works with end2end ocr.
  • det_batch_size and recog_batch_size arguments define the number of images you want to forward to the model at the same time. For maximum speed, set this to the highest number you can. The max batch size is limited by the model complexity and the GPU VRAM size.

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