Classifying characters in scikit-learn

Let's apply SVMs to a classification problem. In recent years, SVMs have been used successfully in the task of character recognition. Given an image, the classifier must predict the character that is depicted. Character recognition is a component of many optical character recognition systems. Even small images require high-dimensional representations when raw pixel intensities are used as features. If the classes are linearly inseparable and must be mapped to a higher dimensional feature space, the dimensions of the feature space can become even larger. Fortunately, SVMs are suited to working with such data efficiently. First we will use scikit-learn to train a SVM to recognize handwritten digits. Then we will work on a more challenging problem: recognizing alphanumeric characters in photographs.

Classifying handwritten digits

The Mixed National Institute of Standards and Technology (MNIST) database is a collection of 70,000 images of handwritten digits...