Meaningful Optical Character Recognition from identity cards with Deep Learning.
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.
- 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.
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
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
text_recognition
Initiating theTextRecognizer
with identity image and then finding the texts with their frames:
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
:
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
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
Sample command line usage
- Optical Character Recognition
python -m mocr --image tests/data/sample_uk_identity_card.png --east tests/model/frozen_east_text_detection.pb
- Face detection from image file
python -m mocr --image-face 'tests/data/sample_de_identity_card.jpg'
- Face detection from video file
python -m mocr --video-face 'tests/data/face-demographics-walking.mp4'
Before
After