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https://github.com/sirfz/tesserocr

A Python wrapper for the tesseract-ocr API
https://github.com/sirfz/tesserocr

cython ocr optical-character-recognition python-library tesseract

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A Python wrapper for the tesseract-ocr API

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README 

        
=========

tesserocr

=========



A simple, |Pillow|_-friendly,

wrapper around the ``tesseract-ocr`` API for Optical Character Recognition

(OCR).



.. image:: https://github.com/sirfz/tesserocr/actions/workflows/build.yml/badge.svg

    :target: https://github.com/sirfz/tesserocr/actions/workflows/build.yml

    :alt: Github Actions 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 `simonflueckiger `_ channel to install from Conda:



::



    > conda install -c simonflueckiger tesserocr



Or alternatively the `conda-forge `_ channel:



::



    > 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 `_ and install them via:



::



    > pip install .whl



Build from source

`````````````````



If you need Windows tessocr package and your Python version is not supported by above mentioned project,

you can try to follow `step by step instructions for Windows 64bit` in `Windows.build.md`_.



.. _Windows.build.md: Windows.build.md



tessdata

========



You may need to point to the tessdata path if it cannot be detected automatically. This can be done by setting the ``TESSDATA_PREFIX`` environment variable or by passing the path to ``PyTessBaseAPI`` (e.g.: ``PyTessBaseAPI(path='/usr/share/tessdata')``). The path should contain ``.traineddata`` files which can be found at https://github.com/tesseract-ocr/tessdata.



Make sure you have the correct version of traineddata for your ``tesseract --version``.



You can list the current supported languages on your system using the ``get_languages`` function:



.. code:: python



    from tesserocr import get_languages



    print(get_languages('/usr/share/tessdata'))  # or any other path that applies to your system



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('---------------------------------------------')