In this study, a unique dataset of a scanned seventeenth-century manuscript is presented which up to now has never been read or analysed. The aim of this research is to be able to transcribe this dataset into machine readable text. The approach used in this study is able to convert the document image without any prior knowledge of the text. In fact, the training set used in this study is a synthetic dataset built on the Google Fonts database. A feed forward Deep Neural Network is trained on a set of different features extracted from the Google Font character images. Well established features such as ratio of character width and height as well as pixel count and Freeman Chain Code is used, with the latter being normalised using Fast Fourier Normalisation that has yielded excellent results in other areas but never been used in Handwritten Character Recognition. In fact, the final results show that this particular Freeman Chain Code feature normalisation yielded the best results achieving an accuracy of 55.1% which is three times higher then the standard Freeman Chain Code normalisation method.