Armenian Paleography is a relatively recent field of study, which emerged in the late 19th century. The typologies are fairly well established and paleographers agree on the distinction between four main types of writing in describing Armenian manuscripts. Although these types characterize clearly different and lengthy periods of production, they are less appropriate for more complex tasks, such as precise dating of a document. A neural network composed of a stack of convolutional layers confirms the relevance of the classification, but also highlights considerable disparity within each type. We propose a precise description of the specificities of Armenian letters in order to explore some other possible classifications. Even though the outcomes need to be further developed, the intermediate evaluations show a 8.07% gain with an extended classification.