Donut :doughnut:, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing).

:rocket: 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

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_donut", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_donut/refs/heads/main/images/example.jpg")

# Display results
extracted_data = algo.get_output(1)
print(extracted_data.data)

:sunny: 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.

:pencil: Set algorithm parameters

• model_name (str) - default 'naver-clova-ix/donut-base-finetuned-docvqa': Name of the Donut pre-trained model for VGA. Other models available:
  • naver-clova-ix/donut-base-finetuned-rvlcdip
  • naver-clova-ix/donut-base-finetuned-cord-v1
  • naver-clova-ix/donut-base-finetuned-cord-v2
• prompt (str): question about document understanding for example.
• cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.
• custom_model_folder: custom model folder (optional)
• task_name: in case of custom model, you should specify the corresponding task

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_donut", auto_connect=True)

algo.set_parameters({
    "model_name": "naver-clova-ix/donut-base-finetuned-docvqa",
    "prompt": "What is the date of the document",
    "cuda": "True"
})

wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_donut/refs/heads/main/images/example.jpg")

# Display results
extracted_data = algo.get_output(1)
print(extracted_data.data)

:mag: 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 algorithm
algo = wf.add_task(name="infer_donut", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_donut/refs/heads/main/images/example.jpg")

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