Integrate contributions into your pipeline

The contrib module provides a collection of additional features which could be relevant for your document analysis pipeline. The following sections will give you an overview of the available modules and features.

Available contribution modules

NOTE: To use the contrib module, you need to install the onnxruntime package. You can install it using the following command:

pip install python-doctr[contrib]
# Or
pip install onnxruntime  # pip install onnxruntime-gpu

Here are all contribution modules that are available through docTR:

ArtefactDetector

The ArtefactDetector module provides a set of functions to detect artefacts in the document images, such as logos, QR codes, bar codes, etc. It is based on the YOLOv8 architecture, which is a state-of-the-art object detection model.

from doctr.io import DocumentFile
from doctr.contrib.artefacts import ArtefactDetector

# Load the document
doc = DocumentFile.from_images(["path/to/your/image"])
detector = ArtefactDetector(batch_size=2, conf_threshold=0.5, iou_threshold=0.5)
artefacts = detector(doc)

# Visualize the detected artefacts
detector.show()

You can also use your custom trained YOLOv8 model to detect artefacts or anything else you need. Reference: YOLOv8

NOTE: The YOLOv8 model (no Oriented Bounding Box (OBB) inference supported yet) needs to be provided as onnx exported model with a dynamic batch size.

from doctr.contrib import ArtefactDetector

detector = ArtefactDetector(model_path="path/to/your/model.onnx", labels=["table", "figure"])