opmpro-edwin / ulozto-captcha-breaker

Deep learning model using Tensorflow that breaks ulozto captcha codes.

ulozto-captcha-breaker

Deep learning model using Tensorflow that breaks ulozto captcha codes.

Algorithm used will be described in a standalone document.

Model specifications

• Input shape: (batch_size, height, width, 1), where height = 70, width = 175
• Output shape: (batch_size, number_of_letters, number_of_classes), where number_of_letters = 4 and number_of_classes = 26

Note that it takes grayscale images on the input. RGB images therefore have to be converted.

How to use pretrained model in your project

Prerequisities

Packages

• numpy~=1.18.3
• tensorflow~=2.1.0

Steps

1. Go to latest release and download binary files
2. Load model in your project using model = tf.keras.models.load_model(PATH_TO_MODEL_DIR)

• PATH_TO_MODEL_DIR is path to directory containing the neural network pretrained model
• it can be found inside the release binary files

3. Normalize image to 0..1 interval. If it already is, skip this step.

img = (img / 255).astype(np.float32)

4. Predict using following code

# convert to grayscale
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
input = 0.299 * r + 0.587 * g + 0.114 * b

# input has now shape (70, 175)
# we modify dimensions to match model's input
input = np.expand_dims(input, 0)
input = np.expand_dims(input, -1)

# input is now of shape (batch_size, 70, 175, 1)
# output will have shape (batch_size, 4, 26)
output = model.predict(input)
# now get labels
labels_indices = np.argmax(output, axis=2)

available_chars = "abcdefghijklmnopqrstuvwxyz"
def decode(li):
    result = []
    for char in li:
        result.append(available_chars[char])
    return "".join(result)

# variable labels will contain read captcha codes
labels = [decode(x) for x in labels_indices]

• tf is alias for tensorflow package, np for numpy