Command line tool for Kraken text segmentation and recognition. Made for OCR4all.
Note
Tested Python version: 3.11.x
pyenv install 3.11.9
pyenv virtualenv 3.11.9 octopy
pyenv activate octopyClone modified Kraken version
git clone --single-branch --branch octopy https://github.com/jahtz/krakenInstall modified package
pip install ./kraken
Clone repository
git clone --recurse-submodules https://github.com/jahtz/octopyNote
CUDA >=12.1 , v12.5 working.
For GPU computation:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/lib:/opt/cuda/lib64python octopy --help> python octopy segtrain --help
Usage: octopy segtrain [OPTIONS] GROUND_TRUTH...
Train a segmentation model using Kraken.
This tool allows you to train a segmentation model from PageXML files, with
options for using pre-trained models, customizing evaluation, and setting
advanced training configurations.
Images should be stored in the same directory as their corresponding PageXML
files.
GROUND_TRUTH: List of PageXML files containing ground truth data for
training. Supports file paths, wildcards, or directories (used with the -g
option).
Options:
--help Show this message and exit.
-o, --output DIRECTORY Directory where training checkpoints and
results will be saved. The directory will be
created if it does not already exist.
[required]
-g, --gt-glob TEXT Specify a glob pattern to match PageXML files
within directories passed to GROUND_TRUTH.
[default: *.xml]
-e, --evaluation DIRECTORY Optional directory containing files for model
evaluation.
-e, --eval-glob TEXT Specify a glob pattern to match evaluation
PageXML files in the --evaluation directory.
[default: *.xml]
-p, --partition FLOAT If no evaluation directory is provided, this
option splits the GROUND_TRUTH files into
training and evaluation sets. Default split
is 90% training, 10% evaluation. [default:
0.9]
-m, --model FILE Optional model to start from. If not set,
training will start from scratch
-n, --name TEXT Name of the output model. Results in
filenames such as <name>_best.mlmodel
[default: foo]
-d, --device TEXT Select the device to use for training (e.g.,
`cpu`, `cuda:0`). Refer to PyTorch
documentation for supported devices.
[default: cpu]
--frequency FLOAT Frequency at which to evaluate model on
validation set. If frequency is greater than
1, it must be an integer, i.e. running
validation every n-th epoch. [default: 1.0]
-q, --quit [early|fixed] Stop condition for training. Choose `early`
for early stopping or `fixed` for a fixed
number of epochs. [default: fixed]
-l, --lag INTEGER RANGE For early stopping, the number of validation
steps without improvement (measured by
val_mean_iu) to wait before stopping.
[default: 10; x>=1]
--epochs INTEGER Number of epochs to train when using fixed
stopping. [default: 50]
--min-epochs INTEGER Minimum number of epochs to train before
early stopping is allowed. [default: 0]
-r, --resize [union|new|fail] Controls how the model's output layer is
resized if the training data contains
different classes. `union` adds new classes,
`new` resizes to match the training data, and
`fail` aborts training if there is a
mismatch. `new` is recommended. [default:
fail]
--workers INTEGER RANGE Number of worker processes for CPU-based
training. [default: 1; x>=1]
--threads INTEGER RANGE Number of threads to use for CPU-based
training. [default: 1; x>=1]
--suppress-regions Disable region segmentation training.
--suppress-baselines Disable baseline segmentation training.
-vr, --valid-regions TEXT Comma-separated list of valid regions to
include in the training. This option is
applied before region merging.
-vb, --valid-baselines TEXT Comma-separated list of valid baselines to
include in the training. This option is
applied before baseline merging.
-mr, --merge-regions TEXT Region merge mapping. One or more mappings of
the form `$target:$src`, where $src is merged
into $target.
-mb, --merge-baselines TEXT Baseline merge mapping. One or more mappings
of the form `$target:$src`, where $src is
merged into $target.
-v, --verbose Set verbosity level for logging. Use -vv for
maximum verbosity (levels 0-2).
> python octopy segment --help
Usage: octopy segment [OPTIONS] [IMAGES]...
Segment images using Kraken and save the results as XML files (PageXML
format).
This tool processes one or more images and segments them using a trained
Kraken model. The segmented results are saved as XML files, corresponding
to each input image.
IMAGES: List of image files to be segmented. Supports multiple file paths,
wildcards, or directories (when used with the -g option).
Options:
--help Show this message and exit.
-g, --glob TEXT Specify a glob pattern to match image files
when processing directories in IMAGES.
[default: *]
-m, --model FILE Path to a custom segmentation model. If not
provided, the default Kraken model is used.
Multiple models can be specified.
-o, --output DIRECTORY Directory to save the output PageXML files.
Defaults to the parent directory of each
input image.
--text-direction [l2r|r2l|t2b|b2t]
Set the text direction for segmentation.
[default: l2r]
-s, --suffix TEXT Append a suffix to the output PageXML file
names. For example, using `.seg.xml` results
in filenames like `imagename.seg.xml`
[default: .xml]
-c, --creator TEXT Specify the creator of the PageXML file.
This can be useful for tracking the origin
of segmented files.
-d, --device TEXT Specify the device for running the model
(e.g., `cpu`, `cuda:0`). Refer to PyTorch
documentation for supported devices.
[default: cpu]
--default-polygon <INTEGER INTEGER INTEGER INTEGER>...
If the polygonizer fails to create a polygon
around a baseline, use this option to create
a default polygon instead of discarding the
baseline. The offsets are defined as left,
top, right, and bottom
--sort-lines Sort text lines in each TextRegion based on
their centroids according to the specified
text direction. (Feature not yet
implemented)
--drop-empty Automatically drop empty TextRegions from
the output. (Feature not yet implemented)
- Octopy:
This project is using Apache-2.0 Open Source license. - Kraken:
Octopy is using a modified version of Kraken v5.2.9.
Kraken is licensed under Apache-2.0 Open Source license.
Changes:
- blla.py, segmentation.py: Adding default_polygon option if polygonizer fails.
Developed at Centre for Philology and Digitality (ZPD), University of Würzburg.