DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition

For more details about this package, make sure to see the documentation available at https://atr.pages.teklia.com/dan/.

Development

For development and tests purpose it may be useful to install the project as a editable package with pip.

• Use a virtualenv (e.g. with virtualenvwrapper mkvirtualenv -a . dan)
• Install dan as a package (e.g. pip install -e .)

Linter

Code syntax is analyzed before submitting the code.
To run the linter tools suite you may use pre-commit.

pip install pre-commit
pre-commit run -a

Run tests

Tests are executed with tox using pytest. To install tox,

pip install tox
tox

To reload the test virtual environment you can use tox -r

Run a single test module: tox -- <test_path> Run a single test: tox -- <test_path>::<test_function>

The tests use a large file stored via Git-LFS. Make sure to run git-lfs pull before running them.

Update documentation

Please keep the documentation updated when modifying or adding features. It's pretty easy to do:

pip install -r doc-requirements.txt
mkdocs serve

You can then write in Markdown in the relevant docs/*.md files, and see live output on http://localhost:8000.

Inference

To apply DAN to an image, one needs to first add a few imports and to load an image. Note that the image should be in RGB.

import cv2
from dan.ocr.predict.inference import DAN

image = cv2.cvtColor(cv2.imread(IMAGE_PATH), cv2.COLOR_BGR2RGB)

Then one can initialize and load the trained model with the parameters used during training.

model_path = "model.pt"
params_path = "parameters.yml"
charset_path = "charset.pkl"

model = DAN("cpu")
model.load(model_path, params_path, charset_path, mode="eval")

To run the inference on a GPU, one can replace cpu by the name of the GPU. In the end, one can run the prediction:

text, confidence_scores = model.predict(image, confidences=True)

Training

This package provides three subcommands. To get more information about any subcommand, use the --help option.

Get started

See the dedicated page on the official DAN documentation.

Data extraction from Arkindex

See the dedicated page on the official DAN documentation.

Model training

See the dedicated page on the official DAN documentation.

Model prediction

See the dedicated page on the official DAN documentation.