The code layout contained within the root of this directory is separated into three distinct parts.

• benchmarking - This directory serves as the local and external benchmarking tests to be used to test our segmentation and recognition model.
• generate_train - This directory serves as a hub to generate data and train on the fly for our recognition model or to generate a CSV file containing all the data needed to train our segmentation model.
• utils - This directory serves as a repository to hold only tools to validate data or help construct any framework.

Overall, the integrity of this project was to illustrate how using Python CAPTCHA libraries to train a CNN model relatively quick and with conjunction of other modules, crack external CAPTCHA imagery.

• Operating System(s): Windows 10 x64, Ubuntu 16.04 amd64
• Video Card(s): Nvidia GTX 1070 8GB
• RAM: 16GB
• Anaconda Virtual Environment for Python codebase execution

• Python Version 3.6.8
• requirements.txt satisfies package dependencies
• Optional - We highly recommend that CUDA is installed on any compatible machine. To install CUDA, follow this tutorial.

Begin the setup process by opening Anaconda Virtual Environment, if your on a Linux system then odds are your Anaconda installation has been exported to your current bash session. If not, then a launcher should be readily available (This is the case on Windows machines).

Environment Setup

$ conda create -n ENVIRONMENT_NAME python=3.6 pip

Activating the Environment

$ conda activate ENVIRONMENT_NAME

Installing requirements.txt

$ pip install -r requirements.txt

Setup your paths Upon every activation of your Anaconda Virtual Environment, the respective run file(s) must be ran in order for Python to properly communicate with the various directories contained within the project.

Windows

> run.bat

Linux

$ source run.src

Navigate to the generate_train/ directory within the root of the repository, here there are two files to assist with either building the segmentation framework data or to train our recognition model.

Data generation for the segmentation model Our segmentation model comes from TensorFlows Object Detection repository on the TensorFlow GitHub repository. If you want to build your own segmentation model then I suggest following this tutorial as it has outlined key points and has covered a great deal of detail into generating a model. Our base model for the segmentation model is the atrous model found within TensorFlows model zoo repository on GitHub.

To generate the data, invoke:

$ python od_generate.py -s NUMBER_OF_SAMPLES -l MAX LENGTH OF CAPTCHA -f fonts/ -d NEW DIRECTORY -t NUMBER OF THREADS

Generate and train the recognition model This part of the process doesnt require external CSV files being generated, rather its done all in memory. Batch size (-b) controls the amount of files generated per batch to be sent to the model to be trained upon. Generations (-g) controls the amount of generations done until model completion and can be stopped at any time as the model is saved per generation.

Sample

$ python generate_train.py -b 8192 -g 5 -f fonts/

After generating our recognition model, we must first install the segmentation model framework into our benchmarking directory. This file may be found on our Releases Page on this GitHub repository. Instructions are provided but in short, make sure the inference_graph/ directory is placed within the root of the benchmarking/ directory.

Place the generated models/ directory inside the root of the benchmarking directory.

Finally we have two files inside this directory to assist in benchmarking your newly created model.

This benchmark focuses on the CAPTCHA we created and testing our TOD framework against our Peak Segmentation framework.

$ python local_benchmark.py

The resulting output provides detailed information and imagery to understand where our model faults.

Before March, a Python script was setup to rip CAPTCHAs off a specified website. They have since updated their site to reCAPTCHA v3 whose CAPTCHA may not be ripped. The resulting directory is of numerous samples taken from the site.

$ python external_benchmark.py --data EXTERNAL_CSV

The utils/ directory located in the root of the repository contains a script helper.py which assists in certain training aspects of the model. Within this script there contains a set of characters to be used in the training and evaluation of our model, keeps this constant during the whole training and evaluation process.

training_characters = ['A','B','C','D','E','F','G','H','J','K','L','M','N','P','Q','R','S','T','U','V','W','X','Y','Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '!', '@', '#', '$','%', '^', '&', '*']

The framework was the first attempt at segmenting the characters and has since been deprecated and serves as a baseline for comparison of the success of the TOD segmentation framework.