Note: The text line recognizer has been ported to C++ and is now a separate project, the CLSTM project, available here: https://github.com/tmbdev/clstm
Python-based OCR package using recurrent neural networks.
To install OCRopus dependencies system-wide:
$ sudo apt-get install $(cat PACKAGES) $ wget -nd http://www.tmbdev.net/en-default.pyrnn.gz $ mv en-default.pyrnn.gz models/ $ sudo python setup.py install
Alternatively, dependencies can be installed into a [Python Virtual Environment] (http://docs.python-guide.org/en/latest/dev/virtualenvs/):
$ virtualenv ocropus_venv/ $ source ocropus_venv/bin/source $ pip install -r requirements_1.txt # tables has some dependencies which must be installed first: $ pip install -r requirements_2.txt $ wget -nd http://www.tmbdev.net/en-default.pyrnn.gz $ mv en-default.pyrnn.gz models/
To test the recognizer, run:
OCRopus is really a collection of document analysis programs, not a turn-key OCR system.
In addition to the recognition scripts themselves, there are a number of scripts for ground truth editing and correction, measuring error rates, determining confusion matrices, etc. OCRopus commands will generally print a stack trace along with an error message; this is not generally indicative of a problem (in a future release, we'll suppress the stack trace by default since it seems to confuse too many users).
To recognize pages of text, you need to run separate commands: binarization, page layout analysis, and text line recognition. Here is an example for a page of Fraktur text (German); you need to download the Fraktur model from tmbdev.net/ocropy/fraktur.pyrnn.gz to run this example:
# perform binarization ./ocropus-nlbin tests/ersch.png -o book # perform page layout analysis ./ocropus-gpageseg 'book/????.bin.png' # perform text line recognition (on four cores, with a fraktur model) ./ocropus-rpred -Q 4 -m models/fraktur.pyrnn.gz 'book/????/??????.bin.png' # generate HTML output ./ocropus-hocr 'book/????.bin.png' -o ersch.html # display the output firefox ersch.html
There are some things the currently trained models for ocropus-rpred will not handle well, largely because they are nearly absent in the current training data. That includes all-caps text, some special symbols (including "?"), typewriter fonts, and subscripts/superscripts. This will be addressed in a future release, and, of course, you are welcome to contribute new, trained models.
You can also generate training data using ocropus-linegen:
ocropus-linegen -t tests/tomsawyer.txt -f tests/DejaVuSans.ttf
This will create a directory "linegen/..." containing training data suitable for training OCRopus with synthetic data.
CLSTM vs OCRopy
The CLSTM project (https://github.com/tmbdev/clstm) is a replacement for
ocropus-rpred in C++ (it used to be a subproject of
ocropy but has been moved into a separate project now). It is significantly faster than
the Python versions and has minimal library dependencies, so it is suitable
for embedding into C++ programs.
Python and C++ models can not be interchanged, both because the save file formats are different and because the text line normalization is slightly different. Error rates are about the same.
In addition, the C++ command line tool (
clstmctc) has different command line
options and currently requiresloading training data into HDF5 files, instead
of being trained off a list of image files directly (image file-based training
will be added to
The CLSTM project also provides LSTM-based language modeling that works very
well with post-processing and correcting OCR output, as well as solving a number
of other OCR-related tasks, such as dehyphenation or changes in orthography
(see our publications). You can train language models using
Generally, your best bet for CLSTM and OCRopy is to rely only on the command line tools; that makes it easy to replace different components. In addition, you should keep your OCR training data in .png/.gt.txt files so that you can easily retrain models as better recognizers become available.
After making CLSTM a full replacement for
next step will be to replace the binarization, text/image segmentation, and layout
analysis in OCRopus with trainable 2D LSTM models.