docling-layout-heron EU AI Act Compliance Profile

Document Layout Analysis "heron"

🚀 heron is the default layout analysis model of the Docling project, designed for robust and high-quality document layout understanding.

📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: "Advanced Layout Analysis Models for Docling", Nikolaos Livathinos et al., 🔗 https://arxiv.org/abs/2509.11720

Inference code example

Prerequisites:

pip install transformers Pillow torch requests

Prediction:

import requests
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
import torch
from PIL import Image


classes_map = {
    0: "Caption",
    1: "Footnote",
    2: "Formula",
    3: "List-item",
    4: "Page-footer",
    5: "Page-header",
    6: "Picture",
    7: "Section-header",
    8: "Table",
    9: "Text",
    10: "Title",
    11: "Document Index",
    12: "Code",
    13: "Checkbox-Selected",
    14: "Checkbox-Unselected",
    15: "Form",
    16: "Key-Value Region",
}
image_url = "https://huggingface.co/spaces/ds4sd/SmolDocling-256M-Demo/resolve/main/example_images/annual_rep_14.png"
model_name = "docling-project/docling-layout-heron"
threshold = 0.6


# Download the image
image = Image.open(requests.get(image_url, stream=True).raw)
image = image.convert("RGB")

# Initialize the model
image_processor = RTDetrImageProcessor.from_pretrained(model_name)
model = RTDetrV2ForObjectDetection.from_pretrained(model_name)

# Run the prediction pipeline
inputs = image_processor(images=[image], return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
results = image_processor.post_process_object_detection(
    outputs,
    target_sizes=torch.tensor([image.size[::-1]]),
    threshold=threshold,
)

# Get the results
for result in results:
    for score, l

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