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A simple, Pillow-friendly, Python wrapper around tesseract-ocr API using Cython

Project description


A simple, |Pillow|_-friendly,
wrapper around the ``tesseract-ocr`` API for Optical Character Recognition

.. image::
:alt: TravisCI build status

.. image::
:alt: Latest version on PyPi

.. image::
:alt: Supported python versions

**tesserocr** integrates directly with Tesseract's C++ API using Cython
which allows for a simple Pythonic and easy-to-read source code. It
enables real concurrent execution when used with Python's ``threading``
module by releasing the GIL while processing an image in tesseract.

**tesserocr** is designed to be |Pillow|_-friendly but can also be used
with image files instead.

.. |Pillow| replace:: ``Pillow``
.. _Pillow:


Requires libtesseract (>=3.04) and libleptonica (>=1.71).

On Debian/Ubuntu:


$ apt-get install tesseract-ocr libtesseract-dev libleptonica-dev

You may need to `manually compile tesseract`_ for a more recent version. Note that you may need
to update your ``LD_LIBRARY_PATH`` environment variable to point to the right library versions in
case you have multiple tesseract/leptonica installations.

|Cython|_ (>=0.23) is required for building and optionally |Pillow|_ to support ``PIL.Image`` objects.

.. _manually compile tesseract:
.. |Cython| replace:: ``Cython``
.. _Cython:

Linux and BSD/MacOS

$ pip install tesserocr

The setup script attempts to detect the include/library dirs (via |pkg-config|_ if available) but you
can override them with your own parameters, e.g.:


$ CPPFLAGS=-I/usr/local/include pip install tesserocr



$ python build_ext -I/usr/local/include

Tested on Linux and BSD/MacOS

.. |pkg-config| replace:: **pkg-config**
.. _pkg-config:

The proposed downloads consist of stand-alone packages containing all the Windows libraries needed for execution. This means that no additional installation of tesseract is required on your system.

You can use the channel `simonflueckiger <>`_ to install from Conda:
> conda install -c simonflueckiger tesserocr

or to get **tesserocr** compiled with **tesseract 4.0.0**:
> conda install -c simonflueckiger/label/tesseract-4.0.0-master tesserocr

Download the wheel file corresponding to your Windows platform and Python installation from `simonflueckiger/tesserocr-windows_build/releases <>`_ and install them via:
> pip install <package_name>.whl


Initialize and re-use the tesseract API instance to score multiple

.. code:: python

from tesserocr import PyTessBaseAPI

images = ['sample.jpg', 'sample2.jpg', 'sample3.jpg']

with PyTessBaseAPI() as api:
for img in images:
print api.GetUTF8Text()
print api.AllWordConfidences()
# api is automatically finalized when used in a with-statement (context manager).
# otherwise api.End() should be explicitly called when it's no longer needed.

``PyTessBaseAPI`` exposes several tesseract API methods. Make sure you
read their docstrings for more info.

Basic example using available helper functions:

.. code:: python

import tesserocr
from PIL import Image

print tesserocr.tesseract_version() # print tesseract-ocr version
print tesserocr.get_languages() # prints tessdata path and list of available languages

image ='sample.jpg')
print tesserocr.image_to_text(image) # print ocr text from image
# or
print tesserocr.file_to_text('sample.jpg')

``image_to_text`` and ``file_to_text`` can be used with ``threading`` to
concurrently process multiple images which is highly efficient.

Advanced API Examples

GetComponentImages example:

.. code:: python

from PIL import Image
from tesserocr import PyTessBaseAPI, RIL

image ='/usr/src/tesseract/testing/phototest.tif')
with PyTessBaseAPI() as api:
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
print 'Found {} textline image components.'.format(len(boxes))
for i, (im, box, _, _) in enumerate(boxes):
# im is a PIL image object
# box is a dict with x, y, w and h keys
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocrResult = api.GetUTF8Text()
conf = api.MeanTextConf()
print (u"Box[{0}]: x={x}, y={y}, w={w}, h={h}, "
"confidence: {1}, text: {2}").format(i, conf, ocrResult, **box)

Orientation and script detection (OSD):

.. code:: python

from PIL import Image
from tesserocr import PyTessBaseAPI, PSM

with PyTessBaseAPI(psm=PSM.AUTO_OSD) as api:
image ="/usr/src/tesseract/testing/eurotext.tif")

it = api.AnalyseLayout()
orientation, direction, order, deskew_angle = it.Orientation()
print "Orientation: {:d}".format(orientation)
print "WritingDirection: {:d}".format(direction)
print "TextlineOrder: {:d}".format(order)
print "Deskew angle: {:.4f}".format(deskew_angle)

or more simply with ``OSD_ONLY`` page segmentation mode:

.. code:: python

from tesserocr import PyTessBaseAPI, PSM

with PyTessBaseAPI(psm=PSM.OSD_ONLY) as api:

os = api.DetectOS()
print ("Orientation: {orientation}\nOrientation confidence: {oconfidence}\n"
"Script: {script}\nScript confidence: {sconfidence}").format(**os)

more human-readable info with tesseract 4+ (demonstrates LSTM engine usage):

.. code:: python

from tesserocr import PyTessBaseAPI, PSM, OEM

with PyTessBaseAPI(psm=PSM.OSD_ONLY, oem=OEM.LSTM_ONLY) as api:

os = api.DetectOrientationScript()
print ("Orientation: {orient_deg}\nOrientation confidence: {orient_conf}\n"
"Script: {script_name}\nScript confidence: {script_conf}").format(**os)

Iterator over the classifier choices for a single symbol:

.. code:: python

from tesserocr import PyTessBaseAPI, RIL, iterate_level

with PyTessBaseAPI() as api:
api.SetVariable("save_blob_choices", "T")
api.SetRectangle(37, 228, 548, 31)

ri = api.GetIterator()
level = RIL.SYMBOL
for r in iterate_level(ri, level):
symbol = r.GetUTF8Text(level) # r == ri
conf = r.Confidence(level)
if symbol:
print u'symbol {}, conf: {}'.format(symbol, conf),
indent = False
ci = r.GetChoiceIterator()
for c in ci:
if indent:
print '\t\t ',
print '\t- ',
choice = c.GetUTF8Text() # c == ci
print u'{} conf: {}'.format(choice, c.Confidence())
indent = True
print '---------------------------------------------'

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