Image Recognition and Python Part 1

Sample code for this series: http://pythonprogramming.net/image-recognition-python/ There are many applications for image recognition. One of the largest that people are most familiar with would be facial recognition, which is the art of matching faces in pictures to identities. Image recognition goes much further, however. It can allow computers to translate written text on paper into digital text, it can help the field of machine vision, where robots and other devices can recognize people and objects. Here, our goal is to begin to use machine learning, in the form of pattern recognition, to teach our program what text looks like. In this case, we'll use numbers, but this could translate to all letters of the alphabet, words, faces, really anything at all. The more complex the image, the more complex the code will need to become. When it comes to letters and characters, it is relatively simplistic, however. How is it done? Just like any problem, especially in programming, we need to just break it down into steps, and the problem will become easily solved. Let's break it down! First, we know we want to show the program an image, and have it compare it to patterns that it knows to make an educated guess on what the current image is. This means we're going to need some "memory" of sorts, filled with examples. In the case of this tutorial, we'd like to do image recognition for the numbers zero through nine. So we'd like to be able to show it any random 2, and have it know the image to be a 2 based on the previous examples of 2's that it has seen and memorized. Next, we need to consider how we'll do this. A computer doesn't read text like we read text. We naturally put things together into a pattern, but a machine just reads the data. In the case of a picture, it reads in the image data, and displays, pixel by pixel, what it is told to display. Past that, a machine makes no attempt to decide whether it is showing a couch or a bird. So, our database of what examples