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.

Python 3.9+ is required. tesserocr no longer supports Python 2.

Building from source 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 (>=3.1.8,<3.4) is required for building and optionally Pillow to support PIL.Image objects.

Install from PyPI:

$ pip install tesserocr

Pre-built wheels for Linux, macOS, and 64-bit Windows bundle Tesseract, Leptonica, and their runtime dependencies. No separate Tesseract installation is required to use these wheels. Language data must still be installed separately (see tessdata).

Alternatively, install from the conda-forge channel:

$ conda install -c conda-forge tesserocr

If a wheel is not available for your platform or Python version, pip will build from source using the dependencies listed above.

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

For Windows source builds, see 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 system

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'))

tesserocr supports free-threaded CPython without enabling the GIL on import. image_to_text and file_to_text can process independent images concurrently. The same ownership rules apply with or without the GIL:

• Use a separate PyTessBaseAPI instance per worker. Instances and their iterators have no internal lock. Do not call an active instance from another thread or re-enter it from a signal handler, including Clear and End.
• Keep the API alive while using its iterators. Calls such as Init, SetImage, Recognize, Clear and End invalidate existing iterators.
• Some Tesseract parameters, particularly in classify and textord, are process-wide even with separate API instances. Configure these before starting parallel native work and do not change them while it runs. This applies to SetVariable, SetDebugVariable, config files and InitFull variables.
• Leptonica's log level and Tesseract's persistent cache are also process-wide. Set set_leptonica_log_level and call ClearPersistentCache only while no native work is running. Use separate processes for independent settings that must change during OCR.

Recognize, GetUTF8Text, image_to_text and file_to_text use Tesseract's cooperative cancellation to propagate Python signal exceptions (such as KeyboardInterrupt) after recognition unwinds safely. Python handles signals only on the main thread: interrupting the main thread does not cancel OCR running in a worker. Layout analysis and other native operations without cancellation checkpoints may delay signal handling until they return.

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(f'Found {len(boxes)} textline image components.')
    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(f"Box[{i}]: x={box['x']}, y={box['y']}, w={box['w']}, h={box['h']}, "
              f"confidence: {conf}, text: {ocrResult}")

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 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(f'symbol {symbol}, conf: {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(f'{choice} conf: {c.Confidence()}')
            indent = True
        print('---------------------------------------------')