Python-based OCR package using recurrent neural networks.
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README.md
README_OSX.md
ocropus-dewarp
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ocropus-errs
ocropus-gpageseg
ocropus-gtedit
ocropus-hocr
ocropus-linegen
ocropus-lpred
ocropus-ltrain
ocropus-nlbin
ocropus-rpred
ocropus-rtrain
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requirements_1.txt
requirements_2.txt
run-coverage
run-rtrain
run-test
setup.py

README.md

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

ocropy

Join the chat at https://gitter.im/tmbdev/ocropy

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:

$ ./run-test

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-rtrain and 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 clstmctc soon).

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 clstmtext.

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 ocropus-rtrain/ocropus-rpred, the next step will be to replace the binarization, text/image segmentation, and layout analysis in OCRopus with trainable 2D LSTM models.

Solution for clang

Read README_OSX.md