/ Machine Learning

OCR Engine based on OCRopy and Kraken

OCR Engine based on OCRopy and Kraken


OCR Engine based on OCRopy and Kraken based on python3. It is designed to both be easy to use from the command line but also be modular to be integrated and customized from other python scripts.


The suggested method is to install calamari into a virtual environment using pip:

virtualenv -p python3 PATH_TO_VENV_DIR (e. g. virtualenv calamari_venv)
source PATH_TO_VENV_DIR/bin/activate
pip install calamari_ocr

which will install calamari and all of its dependencies including Tensorflow as default backend.

To install the package without a virtual environment simply run

pip install calamari_ocr

To install the package from its source, download the source code and run

python setup.py install

Command line interface (Standard User)

If you simply want to use calamari for applying existent models to your text lines and optionally train new models you probably should use the command line interface of calamari, which is very similar to the one of OCRopy.

Note that you have to activate the virtual environment if used during the installation in order to make the command line scripts available.

Prediction of a page

Currently only OCR on lines is supported.
Modules to segment pages into lines will be available soon.
In the meantime you should use the scripts provided by OCRopus.

The prediction step using very deep neural networks implemented on Tensorflow as core feature of calamari should be used:

calamari-predict --checkpoint path_to_model.ckpt --files your_images.*.png

Calamari also supports several voting algorithms to improve different predictions of different models. To enable voting you simply have to pass several models to the --checkpoint argument:

calamari-predict --checkpoint path_to_model_1.ckpt path_to_model_2.ckpt ... --files your_images.*.png

The voting algorithm can be changed by the --voter flag. Possible values are: confidence_voter_default_ctc (default), confidence_voter_fuzzy_ctc, sequence_voter. Note that both confidence voters depend on the loss function used for training a model, while the sequence voter can be used for all models but might yield slightly worse results.

Training of a model

In calamari you can both train a single model using a given data set or train a fold of several (default 5) models to generate different voters for a voted prediction.

Training a single model

A single model can be trained by the calamar-train-script. Given a data set with its ground truth you can train the default model by calling:

calamari-train --files your_images.*.png

Note, that calamari expects that each image file (.png) has a corresponding ground truth text file (.gt.txt) at the same location with the same base name.

There are several important parameters to adjust the training. For a full list type calamari-train --help.

  • --network=cnn=40:3x3,pool=2x2,cnn=60:3x3,pool=2x2,lstm=200,dropout=0.5: Specify the network structure in a simple language. The default network consists of a stack of two CNN- and Pooling-Layers, respectively and a following LSTM layer. The network uses the default CTC-Loss implemented in Tensorflow for training and a dropout-rate of 0.5. The creation string thereto is: cnn=40:3x3,pool=2x2,cnn=60:3x3,pool=2x2,lstm=200,dropout=0.5. To add additional layers or remove a single layer just add or remove it in the comma separated list. Note that the order is important!
  • --line_height=48: The height of each rescaled input file passed to the network.
  • --num_threads=1: The number of threads used during training and line preprocessing.
  • --batch_size=1: The number of lines processed in parallel.
  • --display=1: How often an informative string about the current training process is printed in the shell
  • --output_dir: A path where to store checkpoints
  • --checkpoint_frequency: How often a model shall be written as checkpoint to the drive
  • --max_iters=1000000: The maximum number of training iterations (batches) for training. Note: this is the upper boundary if you use early stopping.
  • --validation=None: Provide a second data set (images with corresponding .gt.txt) to enable early stopping.
  • --early_stopping_frequency=checkpoint_frequency: How often to check for early stopping on the validation dataset.
  • --early_stopping_nbest=10: How many successive models must be worse than the current best model to break the training loop
  • --early_stopping_best_model_output_dir=output_dir: Output dir for the current best model
  • --early_stopping_best_model_prefix=best: Prefix for the best model (output name will be {prefix}.ckpt
  • --n_augmentations=0: Data augmentation on the training set.
  • --weights: Load network weights from a given pretrained model. Note that the codec will probabily change its size to match the codec of the provided ground truth files. To enforce that some characters may not be deleted use a --whitelist.
  • --whitelist=[] --whitelist_files=[]: Specify either individual characters or a text file listing all white list characters stored as string.

Hint: If you want to use early stopping but don't have a separated validation set you can train a single fold of the calamari-cross-fold-train-script (see next section).

Training a n-fold of models

To train n more-or-less individual models given a training set you can use the calamari-cross-fold-train-script. The default call is

calamari-cross-fold-train --files your_images*.*.png --best_models_dir some_dir

By default this will train 5 default models using 80%=(n-1)/n of the provided data for training and 20%=1/n for validation. These independent models can then be used to predict lines using a voting mechanism.
There are several important parameters to adjust the training. For a full list type calamari-cross-fold-train --help.

  • Almost parameters of calamari-train can be used to affect the training
  • --n_folds=5: The number of folds
  • --weights=None: Specify one or n_folds models to use for pretraining.
  • --best_models_dir=REQUIRED: Directory where to store the best model determined on the validation data set
  • --best_model_label={id}: The prefix for each of the best model of each fold. A string that will be formatted. {id} will be replaced by the number of the fold, i. e. 0, ..., n-1.
  • --temporary_dir=None: A directory where to store temporary files, e. g. checkpoints of the scripts to train an individual model. By default a temporary dir using pythons tempfile modules is used.
  • --max_parallel_models=n_folds: The number of models that shall be run in parallel. By default all models are trained in parallel.
  • --single_fold=[]: Use this parameter to train only a subset, e. g. a single fold out of all n_folds.

To use all models to predict and then vote for a set of lines you can use the calamari-predict script and provide all models as checkpoint:

calamari-predict --checkpoint best_models_dir/*.ckpt.json --files your_images.*.png

Evaluating a model

To compute the performance of a model you need first to predict your evaluation data set (see calamari-predict. Afterwards run

calamari-eval --gt *.gt.txt

on the ground truth files to compute an evaluation measure including the full confusion matrix. By default the predicted sentences as produced by the calamari-predict script end in .pred.txt. You can change the default behavior of the validation script by the following parameters

  • --gt=REQUIRED: The ground truth txt files.
  • --pred=None: The prediction files. If None it is expected that the prediction files have the same base name as the ground truth files but with --pred_ext as suffix.
  • --pred_ext=.pred.txt: The suffix of the prediction files if --pred is not specified
  • --n_confusions=-1: Print only the top n_confusions most common errors.

Experimenting with different network hyperparameters (experimental)

To find a good set of hyperparameters (e. g. network structure, learning rate, batch size, ...) you can use the experiment.pyscript that will both train models using the Cross-Fold-Algorithm and evaluate the model on a given evaluation data set. Thereto this script will directly output the performance of each individual fold, the average and its standard deviation, plus the results using the different voting algorithms.
If you want to use this experimental script have a look at the parameters (experiment.py --help).