Learning Data Mining with Python

Beating CAPTCHAs with Neural Networks
The next step in our algorithm for beating these CAPTCHAs involves segmenting
the word to discover each of the letters within it. To do this, we are going to create
a function that finds contiguous sections of black pixels on the image and extract
them as sub-images. These are (or at least should be) our letters.
First we import the label and regionprops functions, which we will use in
this function:
from skimage.measure import label, regionprops
Our function will take an image, and return a list of subimages, where each
sub-image is a letter from the original word in the image:
def segment_image(image):
The first thing we need to do is to detect where each letter is. To do this, we will
use the label function in scikit-image, which finds connected sets of pixels that
have the same value. This has analogies to our connected component discovery
in Chapter 7, Discovering Accounts to Follow Using Graph Mining.
The label function takes an image and returns an array of the same shape as the
original. However, each connected region has a different number in the array and
pixels that are not in a connected region have the value 0. The code is as follows:
labeled_image = label(image > 0)
We will extract each of these sub-images and place them into a list:
subimages = []
The scikit-image library also contains a function for extracting information about
these regions: regionprops. We can iterate over these regions and work on each
individually:
for region in regionprops(labeled_image):
From here, we can query the region object for information about the current region.
For our algorithm, we need to obtain the starting and ending coordinates of the
current region:
start_x, start_y, end_x, end_y = region.bbox
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