A simple, Pillow_-friendly, wrapper around the tesseract-ocr API for Optical Character Recognition (OCR).
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-configYou 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 tesserocrThe 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 tesserocror
$ python setup.py build_ext -I/usr/local/includeTested 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 simonflueckiger channel to install from Conda:
> conda install -c simonflueckiger tesserocrOr alternatively the conda-forge channel:
> conda install -c conda-forge tesserocrDownload 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>.whlIf 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.
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:
from tesserocr import get_languages
print(get_languages('/usr/share/tessdata')) # or any other path that applies to your systemInitialize 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.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:
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.
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))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:
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):
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))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('---------------------------------------------')