ocrd_typegroups_classifier

Typegroups classifier for OCR

Installation

From PyPI

pip3 install ocrd_typegroup_classifier

From source

If needed, create a virtual environment for Python 3 (it was tested successfully with Python 3.7), activate it, and install ocrd.

virtualenv -p python3 ocrd-venv3
source ocrd-venv3/bin/activate
pip3 install ocrd

Enter in the folder containing the tool:

cd ocrd_typegroups_classifier/

Install the module and its dependencies

make install

Finally, run the test:

sh test/test.sh

Models

Densenet

The model densenet121.tgc is based on a DenseNet with 121 layers, and is trained to classify pages or regions, on the following 12 classes:

Antiqua
Bastarda
Fraktur
Gotico-Antiqua
Greek
Hebrew
Italic
Rotunda
Schwabacher
Textura
other_font
not_a_font

The confusion matrix obtained with a DenseNet-121 on the pages with a single font from the dataset (see "Training a classifier" below) is:

	Antiqua	Bastarda	Fraktur	Got.-Ant.	Greek	Hebrew	Italic	Rotunda	Schwabacher	Textura	Other font	Not a font	Recall
Antiqua	1531		10				5	2				5	98.6%
Bastarda		286						6	10	1			94.4
Fraktur			1933					1	5	1		2	99.5%
Gotico-Antiqua				269						1			99.6
Greek					58	1					1		96.7%
Hebrew					1	326							99.7%
Italic			1				187						99.5%
Rotunda				9				1495	5	11		1	98.3%
Schwabacher		16	4					2	452				95.4%
Textura				2						371		1	99.2%
Other font											288	15	94.1%
Not a font	4		2	2	1		5	1	7		4	2331	98.9%
Precision	99.7%	94.7%	99.1%	95.4%	96.7%	99.4%	94.9%	99.1%	94.2%	96.4%	98.3%	99.0%

Colwise classifier

The model colwise_classifier.tgc is based on a pixel column classifier composed of an RNN and a CNN backbone, it is trained to classify text lines on the following 12 classes :

Antiqua
Bastarda
Fraktur
Textura
Schwabacher
Greek
Italic
Hebrew
Gotico-antiqua
Manuscript
Rotunda
no class/ignore

Updating PyTorch

If you update PyTorch, it is possible that the model cannot be loaded anymore. To solve this issue, proceed as follows.

Downgrade to a version of PyTorch which can load the model,
Run the following code:

import torch
from ocrd_typegroups_classifier.typegroups_classifier import TypegroupsClassifier
tgc = TypegroupsClassifier.load('ocrd_typegroups_classifier/models/densenet121.tgc')
torch.save(tgc.model.state_dict(), 'model.pt')

Upgrade to the desired version of PyTorch
Run the following code:

import torch
from ocrd_typegroups_classifier.network.densenet import densenet121
from ocrd_typegroups_classifier.typegroups_classifier import TypegroupsClassifier
print('Creating the network')
net = densenet121(num_classes=12)
net.load_state_dict(torch.load('model.pt'))
print('Creating the classifier')
tgc = TypegroupsClassifier(
    {
        'antiqua':0,
        'bastarda':1,
        'fraktur':2,
        'gotico_antiqua':3,
        'greek':4,
        'hebrew':5,
        'italic':6,
        'rotunda':7,
        'schwabacher':8,
        'textura':9,
        'other_font':10,
        'not_a_font':11
    },
    net
)
tgc.save('ocrd_typegroups_classifier/models/densenet121.tgc')

delete model.mdl

If PyTorch cannot load model.mdl, then you will have to train a new model from scratch.

Training a classifier

The data used for training the densenet classifier provided in this repository is freely available at the following address:

https://doi.org/10.1145/3352631.3352640

The script in tool/create_training_patches.py can be used to extract a suitable amount of crops to train the network, with data balancing.

The script in tools/train_densenet121.py continues the training of any existing densenet121.tgc in the models/ folder. If there is none present, then a new one is created and trained from scratch.

Note that you might have to adapt the paths in these scripts so that they correspond to where data is in your system.

Generating activation heatmaps

For investigation purpose, it is possible to produce heatmaps showing where and how much the densenet network gets activated for specific classes.

You need first to install an additional dependency which is not required by the OCR-D tool with:

pip install tqdm

Then, you can run heatmap.py:

python3 heatmap.py --layer 9 --image_path sample2.jpg

You can specify which layer of the network you are interested in, between 0 and 11. Best results are to be expected with larger values. If no layer is specified, then the 11th is used by default.

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