www.allitebooks.com

Chapter 8
Summary
In this chapter, we worked with images in order to use simple pixel values to
predict the letter being portrayed in a CAPTCHA. Our CAPTCHAs were a bit
simplified; we only used complete four-letter English words. In practice, the problem
is much harder—as it should be! With some improvements, it would be possible to
solve much harder CAPTCHAs with neural networks and a methodology similar to
what we discussed. The scikit-image library contains lots of useful functions for
extracting shapes from images, functions for improving contrast, and other image
tools that will help.
We took our larger problem of predicting words, and created a smaller and simple
problem of predicting letters. From here, we were able to create a feed-forward
neural network to accurately predict which letter was in the image. At this
stage, our results were very good with 97 percent accuracy.
Neural networks are simply connected sets of neurons, which are basic computation
devices consisting of a single function. However, when you connect these together,
they can solve incredibly complex problems. Neural networks are the basis for deep
learning, which is one of the most effective areas of data mining at the moment.
Despite our great per-letter accuracy, the performance when predicting a word
drops to just over 50 percent when trying to predict a whole word. There were
several factors for this, representing the difficulty of taking a problem from an
experiment to the real world.
We improved our accuracy using a dictionary, searching for the best matching
word. To do this, we considered the commonly used edit distance; however, we
simplified it because we were only concerned with individual mistakes on letters,
not insertions or deletions. This improvement netted some benefit, but there are
still many improvements you could try to further boost the accuracy.
In the next chapter, we will continue with string comparisons. We will attempt
to determine which author (out of a set of authors) wrote a particular
document—using only the content and no other information!
[ 183 ]