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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 (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

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.


$ 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


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:
        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:

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:

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):

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:

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 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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