infer_mmlab_text_recognition

Run text recognition algorithms from MMLAB framework. This algorithm will often be applied after a text detection algorithm. You can use infer_mmlab_text_detection from Ikomia HUB for this task.

Models will come from MMLAB's model zoo if custom training is disabled. If not, you can choose to load your model trained with algorithm train_mmlab_detection from Ikomia HUB. In this case, make sure to set parameters for config file (.py) and model file (.pth). Both of these files are produced by the train algorithm.

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add text detection algorithm
text_det = wf.add_task(name="infer_mmlab_text_detection", auto_connect=True)

# Add text recognition algorithm
text_rec = wf.add_task(name="infer_mmlab_text_recognition", auto_connect=True)

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_mmlab_text_recognition/main/images/billboard.jpg")

# Display results
img_output = text_rec.get_output(0)
recognition_output = text_rec.get_output(1)
display(img_output.get_image_with_mask_and_graphics(recognition_output), title="MMLAB text recognition")

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

• If you haven't started using Ikomia Studio yet, download and install it from this page.

• For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add text detection algorithm
text_det = wf.add_task(name="infer_mmlab_text_detection", auto_connect=True)

# Add text recognition algorithm
text_rec = wf.add_task(name="infer_mmlab_text_recognition", auto_connect=True)

text_rec.set_parameters({
    "model_name": "satrn",
    "cfg": "satrn_shallow-small_5e_st_mj.py",
    "config_file": "",
    "model_weight_file": "",
    "batch_size": "64",
    "dict_file": "dicts/english_digits_symbols.txt",
})

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_mmlab_text_recognition/main/images/billboard.jpg")

• model_name (str, default="satrn"): model name.
• cfg (str, default="satrn_shallow-small_5e_st_mj"): name of the model configuration file.
• conf_thres (float, default=0.5): object detection confidence.
• config_file (str, default=""): path to model config file (only if custom_training=True). The file is generated at the end of a custom training. Use algorithm train_mmlab_text_recognition from Ikomia HUB to train custom model.
• model_weight_file (str, default=""): path to model weights file (.pt) (only if custom_training=True). The file is generated at the end of a custom training.
• batch_size (int, default=64): batch processing to speed up inference time.
• dict_file (str, default="dicts/english_digits_symbols.txt"): characters dictionary.

MMLab framework for text recognition offers a large range of models. To ease the choice of couple (model_name/cfg), you can call the function get_model_zoo() to get a list of possible values.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add text recognition algorithm
text_rec = wf.add_task(name="infer_mmlab_text_recognition", auto_connect=True)

# Get list of possible models (model_name, model_config)
print(text_rec.get_model_zoo())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add text detection algorithm
text_det = wf.add_task(name="infer_mmlab_text_detection", auto_connect=True)

# Add text recognition algorithm
text_rec = wf.add_task(name="infer_mmlab_text_recognition", auto_connect=True)

# Run the workflow on image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_mmlab_text_recognition/main/images/billboard.jpg")

# Iterate over outputs
for output in text_rec.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

MMLab text recognition algorithm generates 2 outputs:

1. Forwarded original image (CImageIO)
2. Text detection output (CTextIO)