This module takes a captcha (image) as input, attempts to partition it into discrete segments, each (it hopes) containing a single symbol, and then runs basic vector space search to determine the similarity of each symbol against known characters (whose reference images are included). The objective of this project is to (a) make bboyte's code more accessible and (b) illustrate, in a readable way, the fundamentals of captcha cracking. It's primary goal is clarity and makes no claims or attempts at efficiency, accuracy, or practicality.
This work is a derivation of an original work by @boyter [email protected], http://www.boyter.org/decoding-captchas/ (see origin tutorial at https://web.archive.org/web/20121012023114/http://www.wausita.com/captcha/)
On ubuntu, libjpeg-dev and libpng-dev may be system requirements for the Python Pillow (PIL) library
sudo apt-get install libjpeg-dev
sudo apt-get install libpng-dev
Next, fetch and build the decaptcha library
pip install git+https://github.com/mekarpeles/captcha-decoder.git
The decaptcha library comes with a command line utility called decaptca. Running the command with -h will show a list of options. The argument can be provided a filepath or a url:
usage: decaptcha [-h] [-v] [-l LIMIT] [-c CHANNELS] [-t THRESHOLD] [--min MIN]
[--max MAX] [-o TOLERANCE]
[<img>]
Python captcha cracking utility
positional arguments:
<img> Enter the filesystem path or url of a captcha image
optional arguments:
-h, --help show this help message and exit
-v Displays the decaptcha version
-l LIMIT, --limit LIMIT
Package url
-c CHANNELS, --channels CHANNELS
The number of prominant color channels to keep
-t THRESHOLD, --threshold THRESHOLD
Accuracy threshold for matching decimal [0-1]
--min MIN Filter out colors darker than this [0-256]
--max MAX Filter out colors light than this [0-256]
-o TOLERANCE, --tolerance TOLERANCE
Pixel tolerance for character segmentation. Higher is
more lenient/greedy, lower is strict.
$ decaptcha http://www.mondor.org/img/capex.jpg --min 0 --max 20 --limit 5 --channels 5 --tolerance 7
Character 0 of 7:
t (confidence of 0.839150063096)
e (confidence of 0.827405543276)
Character 1 of 7:
0 (confidence of 0.834057656228)
l (confidence of 0.771064160322)
Character 2 of 7:
t (confidence of 0.309437274354)
e (confidence of 0.303227199152)
Character 3 of 7:
Character 4 of 7:
t (confidence of 0.267644230239)
7 (confidence of 0.266067912114)
Character 5 of 7:
0 (confidence of 0.834057656228)
l (confidence of 0.789422830806)
Character 6 of 7:
t (confidence of 0.835510535512)
e (confidence of 0.835221298415)
The following implementations and techniques are recommended as more practical and accurate alternatives for this project: