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

TravisCI build status Latest version on PyPi

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.


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.


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

Tested on Linux and BSD/MacOS


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.


You can use the conda-forge channel to install from Conda:

> conda install -c conda-forge 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 images:

from tesserocr import PyTessBaseAPI

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

with PyTessBaseAPI() as api:
    for img in images:
# 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:

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

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:

from PIL import Image
from tesserocr import PyTessBaseAPI, RIL

image = Image.open('/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):

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")

    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:

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):

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:

from __future__ import print_function

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), 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