https://github.com/verifid/mocr
Meaningful Optical Character Recognition from identity cards with Deep Learning.
https://github.com/verifid/mocr
cards computer-vision deep-learning face-detection identity opencv optical-character-recognition tessaract
Last synced: 7 months ago
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Meaningful Optical Character Recognition from identity cards with Deep Learning.
- Host: GitHub
- URL: https://github.com/verifid/mocr
- Owner: verifid
- License: mit
- Created: 2018-11-17T21:56:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-02-20T22:16:46.000Z (over 4 years ago)
- Last Synced: 2024-11-09T09:49:02.740Z (7 months ago)
- Topics: cards, computer-vision, deep-learning, face-detection, identity, opencv, optical-character-recognition, tessaract
- Language: Python
- Homepage: https://mocr.verifid.app
- Size: 90.2 MB
- Stars: 26
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: COPYING
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README
mocr
======
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Meaningful Optical Character Recognition from identity cards with Deep Learning.
Introduction
============
**mocr** is a library that can be used to detect meaningful optical characters from identity cards. Code base is pure ``Python`` and
works with 3.x versions. It has some low level dependencies such as ``Tesseract``. **mocr** uses a pre-trained east
detector with OpenCV and applies it's Deep Learning techniques.
It has a pre-trained east detector inside the module and a custom trained model can be given as a parameter.
Prerequisites
=============
* `Tessaract `_ must be installed on your computer before using OCR. Please check `installation link `_ for details.
* The other dependencies are listed on ``requirements.txt`` and will be installed when you install with pip.
Installation
============
**From source**
Install module using `pip`::
$ pip install mocr
Download the latest `mocr` library from: https://github.com/verifid/mocr
Install module using `pip`::
$ pip install -e .
Extract the source distribution and run::
$ python setup.py build
$ python setup.py install
Running Tests
=============
The test suite can be run against a single Python version which requires ``pip install pytest`` and optionally ``pip install pytest-cov`` (these are included if you have installed dependencies from ``requirements.testing.txt``)
To run the unit tests with a single Python version::
$ py.test -v
to also run code coverage::
$ py.test -v --cov-report html --cov=mocr
To run the unit tests against a set of Python versions::
$ tox
Sample Usage
============
* ``text_recognition`` Initiating the ``TextRecognizer`` with identity image and then finding the texts with their frames:
.. code:: python
import os
from mocr import TextRecognizer
image_path = os.path.join('tests', 'data/sample_uk_identity_card.png')
east_path = os.path.join('mocr', 'model/frozen_east_text_detection.pb')
text_recognizer = TextRecognizer(image_path, east_path)
(image, _, _) = text_recognizer.load_image()
(resized_image, ratio_height, ratio_width, _, _) = text_recognizer.resize_image(image, 320, 320)
(scores, geometry) = text_recognizer.geometry_score(east_path, resized_image)
boxes = text_recognizer.boxes(scores, geometry)
results = text_recognizer.get_results(boxes, image, ratio_height, ratio_width)
# results: Meaningful texts with bounding boxes
* ``face_detection``:
.. code:: python
from mocr import face_detection
image_path = 'YOUR_IDENTITY_IMAGE_PATH'
face_image = face_detection.detect_face(image_path)
# face_image is the byte array detected and cropped image from original image
.. code:: python
from mocr import face_detection
video_path = 'YOUR_IDENTITY_VIDEO_PATH'
face_image = face_detection.detect_face_from_video(video_path)
# face_image is the byte array detected and cropped image from original video
CLI
===
Sample command line usage
* Optical Character Recognition
.. code::
python -m mocr --image tests/data/sample_uk_identity_card.png --east tests/model/frozen_east_text_detection.pb
* Face detection from image file
.. code::
python -m mocr --image-face 'tests/data/sample_de_identity_card.jpg'
* Face detection from video file
.. code::
python -m mocr --video-face 'tests/data/face-demographics-walking.mp4'
Screenshots
-----------
**Before**
|image_before|
**After**
|image_after|
.. |image_before| image:: https://raw.githubusercontent.com/verifid/mocr/master/screenshots/sample_uk_identity_card.png
.. |image_after| image:: https://raw.githubusercontent.com/verifid/mocr/master/screenshots/uk_identity_card_after_detection.png