dfine-kraken 0.4.1

Segmentation with D-FINE for kraken

D-FINE for document layout analysis

This is an adaptation//refactor of ArgoHA's implementation of the D-FINE object detector intended to put it in line with community standards in the (historical) document layout analysis community. It is a foundation for eventual integration into the kraken ATR engine.

Installation

Kraken is currently being rewritten to allow integration of new methods, such as the one in this repository, with plug-ins. This repository uses the architecture introduced by this rework which will eventually become kraken 7.0. The models produced by this repository are not going to be compatible with earlier kraken versions.

For the latest stable release run:

$ pip install dfine_kraken

Clone the repository and run:

$ pip install .

This will install a dfine that can be used to train models:

$ dfine ... train ...

Training

The basic syntax is very similar to kraken segmentation training using Page or ALTO XML files. During the rework many of the segmentation dataset filtering and transformation options have disappeared, being replaced by dictionaries mapping class labels to indices. As it is annoying to define mapping on the command line, mappings that do not assign one index to each class in the source data need to be defined in YAML experiment configuration files.

To train a basic model for 50 epochs from scratch:

$ dfine -d cuda:0 train *.xml

The default configuration trains lines and regions jointly. If this is not what you want take a look at the sample configuration file to see how to disable text line detection in bbox format.

Inference

Inference is integrated in kraken. You need to convert the checkpoint into weights first:

$ ketos convert -o dfine.safetensors checkpoint.ckpt

and then run kraken ... segment ... as usual:

$ kraken -i input.jpg out.xml -a segment -i dfine.safetensors

Pretrained Models

Pretrained region segmentation models trained on the LADaS dataset using the SegmOnto taxonomy (37 region types) are available for all model variants. They can be downloaded with kraken download:

Variant	Size	mAP@50	Download command
nano	~15 MB	0.2960	kraken get 10.5281/zenodo.18715384
small	~42 MB	0.3864	kraken get 10.5281/zenodo.18715381
medium	~79 MB	0.3915	kraken get 10.5281/zenodo.18715373
large	~126 MB	0.4308	kraken get 10.5281/zenodo.18715364
extra_large	~252 MB	0.4164	kraken get 10.5281/zenodo.18715367

The large variant achieves the best performance. The extra_large variant shows slight regression likely due to overfitting, so the large model is recommended for best accuracy.

$ kraken get 10.5281/zenodo.18715373
Processing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 79.1/79.1 MB 0:00:00 0:00:02
Model dir: /home/mittagessen/.local/share/htrmopo/d58d541d-82cf-5ab2-b27b-a20fe2548229 (model files: ladas_m.safetensors)
$ kraken -i input.jpg out.xml -a segment -i ladas_m.safetensors
...