In the first part of this article we describe a method for extracting and classifying handwritten annotations on printed documents from textual images taken with a consumer camera. The ambition of such a research is to offer a seamless integration of notes taken on printed paper in our daily interactions with digital documents. Existing studies propose a classification of annotations based on their form and function. We demonstrate a method for automating such a classification and report experimental results showing the classification accuracy. In the second part of the article we provide a road map for conducting user-centered studies using eye-tracking systems aiming to investigate the cognitive roles and social effects of annotations. Based on our understanding of some research questions arising from this experiment, in the last part of the article we describe a social learning environment that facilitates knowledge sharing across a class of students or a group of colleagues through shared annotations.