This release is a pre-release and may not be stable for production use.
A simple, Pillow-friendly, wrapper around the tesseract-ocr API for Optical Image 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 (>=3.02) 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.
Installation
$ python setup.py install
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.
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