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...
