  The type used to print an early modern book can give scholars valuable
information about the time and place of its production as well as its producer.
Recognizing such type is currently done manually using both the character
shapes of `M' or `Qu' and the size of the total type to look it up in a large
reference work. This is a reliable method, but it is also slow and requires
specific skills. We investigate the performance of type classification and type
retrieval using a newly created dataset consisting of easy and difficult types
used in early printed books. For type classification, we rely on a deep
Convolutional Neural Network (CNN) originally used for font-group
classification while we use a common writer identification method for the
retrieval case. We show that in both scenarios, easy types can be
classified/retrieved with a high accuracy while difficult cases are indeed
difficult.
