Required dependencies: python-captcha, opencv, python-tensorflow (CPU or GPU)

./generate.py --width 128 --height 64 --length 4 --symbols symbols.txt --count 3200 --scramble --output-dir test

This generates 3200 128x64 pixel captchas with 4 symbols per captcha, using the set of symbols in the symbols.txt file. The captchas are stored in the folder test, which is created if it doesn't exist. The names of the captcha images are scrambled.

Without the --scramble option, the name of the image is the captcha text.

To train and validate a neural network, we need two sets of data: a big training set, and a smaller validation set. The network is trained on the training set, and tested on the validation set, so it is very important that there are no images that are in both sets.

To generate the training data, the "ground truth" classification for each training example image must be known. This means that for training, the names of the captchas cannot be scrambled, because otherwise the training process has no way to check if the answer from the CNN for some captcha is right or wrong! Make sure not to use the --scramble option when generating the training or validation datasets.

Using the --seed argument to the generator script, you can pass an arbitrary string that will be used as a random seed for captcha generation. This can be an integer, some text, or any other string that's representable in utf-8 encoding.

Setting the seed to the same value will mean the same sequence of captchas is produced. Note that the exact same sequence of images will not be produced, because a given captcha text can be rendered in many different ways. However the same sequence of captcha text samples will be produced.

./train.py --width 128 --height 64 --length 4 --symbols symbols.txt --batch-size 4 --epochs 2 --output-model test.h5 --train-dataset training_data --validate-dataset validation_data

train.py --width 128 --height 64 --length 8 --symbols symbols.txt --batch-size 10 --epochs 20 --output-model modelBWHist --train-dataset training_data --validate-dataset test8

Train the neural network for 2 epochs on the data specified. One epoch is one pass through the full dataset.

The suggested training dataset size for the initial training for captcha length of 4 symbols is 20000 images, with a validation dataset size of 4000 images.

./classify.py  --model-name test --captcha-dir ~/Downloads/validation_data/ --output ~/Downloads/stuff.txt --symbols symbols.txt

With --model-name test the classifier script will look for a model called test.json with weights test.h5 in the current directory, and load the model up.

The classifier runs all the images in --captcha-dir through the model, and saves the file names and the model's guess at captcha contained in the image in the --output file.