README.md

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

## Documentation

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


## Installation

To use DAN in your own scripts, install it using pip:

```console
pip install -e .
```

## 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.
```python
import cv2
from dan.predict 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.
```python
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:
```python
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.

### Data extraction from Arkindex

See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/datasets/extract/) on the official DAN documentation.

### Dataset formatting

See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/datasets/format/) on the official DAN documentation.

### Model training

See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/train/) on the official DAN documentation.

### Synthetic data generation
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/generate/) on the official DAN documentation.

### Model prediction
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/predict/) on the official DAN documentation.