Automatic recognition of handwritten mathematics has enjoyed significant improvements in the past decades. In particular, online recognition of mathematical formulae has seen a number of important advancements. However, in reality most mathematics is still taught and developed on regular whiteboards and offline recognition remains an open and challenging task in this area. In this thesis we develop methods to recognise mathematics from static images of handwritten expressions on whiteboards, while leveraging the strength of online recognition systems by transforming offline data into online information. Our approach is based on trajectory recovery techniques, that allow us to reconstruct the actual stroke information necessary for online recognition. To this end we develop a novel recognition process especially designed to deal with whiteboards by prudently extracting information from colour images. To evaluate our methods we use an online recogniser for the recognition task, which is specifically trained for recognition of maths symbols. We present our experiments with varying quality and sources of images. In particular, we have used our approach successfully in a set of experiments using Google Glass for capturing images from whiteboards, in which we achieve highest accuracies of 88.03% and 84.54% for segmentation and recognition of mathematical symbols respectively.