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Project Description

The package provides tools for recognizing machine readable zones (MRZ) from scanned identification documents. The documents may be located rather arbitrarily on the page - the code tries to find anything resembling a MRZ and parse it from there.

The recognition procedure may be rather slow - around 10 or more seconds for some documents. Its precision is not perfect, yet seemingly decent as far as test documents available to the developer were concerned - in around 80% of the cases, whenever there is a clearly visible MRZ on a page, the system will recognize it and extract the text to the best of the abilities of the underlying OCR engine (Google Tesseract).

The failed examples seem to be most often either clearly badly scanned documents, where text is way too blurred, or, more seriously, some types of IDs (Romanian being one example), where the MRZ is too close to the remaining part of the card - a situation not accounted for too well by the current algorithm.

Installation

The simplest way to install the package is via easy_install or pip:

$ pip install PassportEye

Note that PassportEye depends on numpy, scipy, matplotlib and scikit-image, among other things. The installation of those requirements, although automatic, may take time or fail sometimes for various reasons (e.g. lack of necessary libraries). If this happens, consider installing the dependencies explicitly from the binary packages, such as those provided by the OS distribution or the “wheel” packages. Another convenient option is to use a Python distribution with pre-packaged numpy/scipy/matplotlib binaries (Anaconda Python being a great choice at the moment).

In addition, you must have the Tesseract OCR installed and added to the system path: the tesseract tool must be accessible at the command line.

Usage

On installation, the package installs a standalone tool mrz into your Python scripts path. Running:

$ mrz <filename>

will process a given filename, extracting the MRZ information it finds and printing it out in tabular form. Running mrz --json <filename> will output the same information in JSON. Running mrz --save-roi <roi.png> will, in addition, extract the detected MRZ (“region of interest”) into a separate png file for further exploration. Note that the tool provides a limited support for PDF files – it attempts to extract the first DCT-encoded image from the PDF and applies the recognition on it. This seems to work fine with most scanner-produced one-page PDFs, but has not been tested extensively.

In order to use the recognition function in Python code, simply do:

>> from passporteye import read_mrz
>> mrz = read_mrz(image_filename)

The returned object (unless it is None, which means no ROI was detected) contains the fields extracted from the MRZ along with some metainformation. For the description of the available fields, see the docstring for the passporteye.mrz.text.MRZ class. Note that you can convert the object to a dictionary using the to_dict() method.

If you want to have the ROI reported alongside the MRZ, call the read_mrz function as follows:

>> mrz = read_mrz(image_filename, save_roi=True)

The ROI can then be accessed as mrz.aux['roi'] – it is a numpy ndarray, representing the (grayscale) image region where the OCR was applied.

For more flexibility, you may instead use a MRZPipeline object, which will provide you access to all intermediate computations as follows:

>> from passporteye.mrz.image import MRZPipeline
>> p = MRZPipeline(filename)
>> mrz = p.result

The “pipeline” object stores the intermediate computations in its data dictionary. Although you need to understand the underlying algorithm to make sense of it, sometimes it may provide for insightful visualizations. This code, for example, will plot the binarized version of the original image which is used in the algorithm to extract ROIs alongside the boxes corresponding to the extracted ROIs:

>> imshow(p['img_binary'])
>> for b in p['boxes']:
..     plot(b.points[:,1], b.points[:,0], c='b')
..     b.plot()

Development

If you plan to develop or debug the package, consider installing it by running:

$ python setup.py develop

from within the source distribution. The package contains a basic set of smoke tests. To run those you should first make sure you have pytest installed:

$ pip install pytest

You can then run the tests by typing:

$ py.test

At the root of the source distribution.

The command-line script evaluate_mrz can be used to assess the performance of the current recognition pipeline on a set of sample images: this is useful if you want to see the effects of changes to the code. Just run:

$ evaluate_mrz -j 4

(where -j 4 would request to use 4 cores in parallel). The same script may be used to run the recognition pipeline on a given directory of images, sorting successes and failures, see evaluate_mrz -h for options.

Contributing

Feel free to contribute or report issues via Github: https://github.com/konstantint/PassportEye

Release History

Release History

1.0.1

This version

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1.0

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1.0.0

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TODO: Figure out how to actually get changelog content.

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
PassportEye-1.0.1.zip (35.4 kB) Copy SHA256 Checksum SHA256 Source Sep 24, 2016

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