tesserocr
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.
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.
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
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 conda-forge channel to install from Conda:
> 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 <package_name>.whl
Usage
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.
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:
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))
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))
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.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('---------------------------------------------')
6 Jan 08, 2022
17 Aug 13, 2022
0 Jan 20, 2022
90 Dec 15, 2022
134 Dec 29, 2022
4 Feb 05, 2022
5 Jun 06, 2022
2 Jan 18, 2022
15 Dec 24, 2022
4 Feb 06, 2022
213 Dec 14, 2022
1 Jun 08, 2022
2 Jan 02, 2022
214 Dec 02, 2022
6 Nov 24, 2022
11 Jun 03, 2022
29 Dec 20, 2022
45 Dec 26, 2022
69 Dec 17, 2022
21 Nov 10, 2022