Data generation folders:
-
captcha_generator: Generate "Dataset 1" mentioned in the paper.
-
captcha_generator_split: Generate "Dataset 2" mentioned in the paper.
-
cnn_captcha: The implementation of Deep-CAPTCHA model used to classify "Dataset 1".
-
dynamic_captcha: The implementation of CapsNet model used to classify "Dataset 1".
-
cnn_captcha_split: The implementation of Deep-CAPTCHA model used to classify "Dataset 2".
-
dynamic_captcha_split: The implementation of Deep-CAPTCHA model used to classify "Dataset 2".
4.1 Adapt the CapsNet to the CAPTCHA dataset
- Run
python captcha_generator.py
inside folder captcha_generator to generate "CAPTCHA_4digits_noise" (Dataset 1) - Copy the folder "CAPTCHA_4digits_noise" to
cnn_captcha/data
ordynamic_captcha/data
as the dataset for those two models - Configure the script in
cnn_captcha/local_experiment_scripts
ordynamic_caps_captcha/local_experiment_scripts
- Use
bash cnn_captcha4digit_test.sh
orbash dynamic_captcha4digit_test.sh
to run training jobs. The results will be stored in a folder with the name specified in the script.
4.2 Adapt CapsNet to the CAPTCHA Puzzle Task
- Run
python captcha_generator.py
inside folder captcha_generator_split to generate "CAPTCHA_3digits_noise" (Dataset 2) - Copy the folder "CAPTCHA_3digits_noise" to
cnn_captcha_split/data
ordynamic_caps_captcha_split/data
as the dataset for those two models - Configure the script in
cnn_captcha_split/local_experiment_scripts
ordynamic_caps_captcha_split/local_experiment_scripts
- Use
bash cnn_captcha4digit_test.sh
orbash dynamic_captcha4digit_test.sh
to run training jobs. The results will be stored in a folder with the name specified in the script.