Proof of Concept: Automatic Type Recognition

TL;DR

This paper explores automatic recognition of early modern book types using deep learning and retrieval methods, demonstrating high accuracy for easy cases but acknowledging challenges with difficult types.

Contribution

It introduces a new dataset and applies CNN-based classification and writer identification techniques to automate type recognition in early printed books.

Findings

High accuracy in classifying easy types

Difficulty in recognizing complex or similar types

Deep learning methods outperform manual identification in speed

Abstract

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.