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https://github.com/mekarpeles/captcha-decoder

An elementary captcha decoder written in python
https://github.com/mekarpeles/captcha-decoder

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An elementary captcha decoder written in python

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captcha-decoder

===============



![Build Status](https://travis-ci.org/mekarpeles/captcha-decoder.png)



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

, http://www.boyter.org/decoding-captchas/ (see

origin tutorial at

https://web.archive.org/web/20121012023114/http://www.wausita.com/captcha/)



## Installation



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



## Usage



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]

                     []

    

    Python captcha cracking utility

    

    positional arguments:

                       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.



# Example



    $ 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)



## Further Reading



The following implementations and techniques are recommended as more practical and accurate alternatives for this project:



1. http://www.codeproject.com/Articles/106583/Handwriting-Recognition-Revisited-Kernel-Support-V