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========= tesserocr

A simple, |Pillow|_-friendly, wrapper around the tesseract-ocr API for Optical Character Recognition (OCR).

.. image:: https://travis-ci.org/sirfz/tesserocr.svg?branch=master :target: https://travis-ci.org/sirfz/tesserocr :alt: TravisCI build status

.. image:: https://img.shields.io/pypi/v/tesserocr.svg?maxAge=2592000 :target: https://pypi.python.org/pypi/tesserocr :alt: Latest version on PyPi

.. image:: https://img.shields.io/pypi/pyversions/tesserocr.svg?maxAge=2592000 :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: http://python-pillow.github.io/

Requirements

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

On Debian/Ubuntu:

::

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

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: https://github.com/tesseract-ocr/tesseract/wiki/Compiling .. |Cython| replace:: Cython .. _Cython: http://cython.org/

Installation

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

or

::

$ python setup.py build_ext -I/usr/local/include

Tested on Linux and BSD/MacOS

.. |pkg-config| replace:: pkg-config .. _pkg-config: https://pkgconfig.freedesktop.org/

Windows

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.

The recommended method of installation is via Conda as described below.

Conda


You can use the `conda-forge <https://anaconda.org/conda-forge/tesserocr>`_ channel to install from Conda:

::

    > conda install -c conda-forge tesserocr

pip
```

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

::

    > pip install <package_name>.whl

Usage
=====

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

.. code:: python

    from tesserocr import PyTessBaseAPI

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

    with PyTessBaseAPI() as api:
        for img in images:
            api.SetImageFile(img)
            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 = Image.open('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 = Image.open('/usr/src/tesseract/testing/phototest.tif')
with PyTessBaseAPI() as api:
    api.SetImage(image)
    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 = Image.open("/usr/src/tesseract/testing/eurotext.tif")
        api.SetImage(image)
        api.Recognize()

        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:
        api.SetImageFile("/usr/src/tesseract/testing/eurotext.tif")

        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:
        api.SetImageFile("/usr/src/tesseract/testing/eurotext.tif")

        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 __future__ import print_function

from tesserocr import PyTessBaseAPI, RIL, iterate_level

with PyTessBaseAPI() as api:
    api.SetImageFile('/usr/src/tesseract/testing/phototest.tif')
    api.SetVariable("save_blob_choices", "T")
    api.SetRectangle(37, 228, 548, 31)
    api.Recognize()

    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), end='')
        indent = False
        ci = r.GetChoiceIterator()
        for c in ci:
            if indent:
                print('\t\t ', end='')
            print('\t- ', end='')
            choice = c.GetUTF8Text()  # c == ci
            print(u'{} conf: {}'.format(choice, c.Confidence()))
            indent = True
        print('---------------------------------------------')

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