loghi

# to get started:
git clone [email protected]:knaw-huc/loghi.git
cd loghi

Get the dockers

If you want to build the dockers yourself with the latest code:

# for initial pulling of submodules use:
git submodule update --init --recursive
git pull --recurse-submodules
cd docker
./buildAll.sh

otherwise just go ahead and use the default dockers on dockerhub. They are usually pulled auatomatically when running na-pipeline.sh mentioned later in this document, but you can pull them separately

docker pull loghi/docker.laypa
docker pull loghi/docker.htr
docker pull loghi/docker.loghi-tooling

Inference

But first go to: https://surfdrive.surf.nl/files/index.php/s/YA8HJuukIUKznSP and download a laypa model (for detection of baselines) and a loghi-htr model (for HTR).

suggestion for laypa:

• general

suggestion for loghi-htr that should give some results:

• generic-2023-02-15

It is not perfect, but a good starting point. It should work ok on 17th and 18th century handwritten dutch. For best results always finetune on your own specific data.

edit the na-pipeline.sh using vi, nano, other whatever editor you prefer. We'll use nano in this example

nano na-pipeline.sh

Look for the following lines:

LAYPAMODEL=INSERT_FULL_PATH_TO_YAML_HERE
LAYPAMODELWEIGHTS=INSERT_FULLPATH_TO_PTH_HERE
HTRLOGHIMODEL=INSERT_FULL_PATH_TO_LOGHI_HTR_MODEL_HERE

and update those paths with the location of the files you just downloaded. If you downloaded a zip: you should unzip it first.

if you do not have a NVIDIA-GPU and nvidia-docker setup additionally change

GPU=0

to

GPU=-1

It will then run on CPU, which will be very slow.

Save the file and run it:

./na-pipeline.sh /PATH_TO_FOLDER_CONTAINING_IMAGES

replace /PATH_TO_FOLDER_CONTAINING_IMAGES with a valid directory containing images (.jpg is preferred/tested) directly below it.

The file should run for a short while if you have a good nvidia GPU and nvidia-docker setup. It might be a long while if you just have CPU available. It should work either way, just a lot slower on CPU.

When it finishes without errors a new folder called "page" should be created in the directory with the images. This contains the PageXML output.

Training an HTR model

Input data

Expected structure

training_data_folder
|- training_all_train.txt
|- training_all_val.txt
|- image1_snippets
    |-snippet1.png
    |-snippet2.png

training_all_train.txt should look something something like:

/path/to/training_data_folder/image1_snippets/snippet1.png	textual representation of snippet 1
/path/to/training_data_folder/image1_snippets//snippet2.png text on snippet 2

n.b. path to image and textual representation should be separated by a tab.

Create training data

You can create training data with the following command:

./create_train_data.sh /full/path/to/input /full/path/to/output

/full/path/to/output is /full/path/to/training_data_folder in this example /full/path/to/input is expected to look like:

input
|- image1.png
|- image2.png
|- page
    |- image1.xml
    |- image2.xml

page/image1.xml should contain information about the baselines and should have the textual representation of the text lines.

Change script

nano na-pipeline-train.sh

Find the following lines:

listdir=INSERT_FULL_PATH_TO_TRAINING_DATA_FOLDER
trainlist=INSERT_FULL_PATH_TO_TRAINING_DATA_LIST
validationlist=INSERT_FULL_PATH_TO_VALIDATION_DATA_LIST

In this example:

listdir=/full/path/to/training_data_folder
trainlist=/full/path/to/training_data_folder/train_list.txt
validationlist=/full/path/to/training_data_folder/val_list.txt

if you do not have a NVIDIA-GPU and nvidia-docker setup additionally change

GPU=0

to

GPU=-1

It will then run on CPU, which will be very slow.

Run script

./na-pipline-train.sh

for later updates use:

git pull --recurse-submodules