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.
On Debian/Ubuntu:
$ apt-get install tesseract-ocr libtesseract-dev libleptonica-dev
Optionally requires Cython for building (otherwise the generated
.cpp file is compiled) and Pillow to support PIL.Image objects.
$ pip install tesserocr
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.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.
GetComponentImages example:
from PIL import Image
from tesserocr import PyTessBaseAPI
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):
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)Iterator over the classifier choices for a single symbol:
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),
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 '---------------------------------------------'