Deep Learning Approaches to Classification of Production Technology for 19th Century Books

TL;DR

This paper explores deep learning methods to classify 19th-century book illustrations by production technology, revealing challenges in achieving high accuracy due to complex visual features and error sources.

Contribution

It introduces a classification approach for historical book images based on production technology using deep learning, highlighting the difficulties and error analysis involved.

Findings

Classification accuracy around 70% for complex images

Identification of key error sources affecting performance

Insights into visual features distinguishing production technologies

Abstract

Cultural research is dedicated to understanding the processes of knowledge dissemination and the social and technological practices in the book industry. Research on children books in the 19th century can be supported by computer systems. Specifically, the advances in digital image processing seem to offer great opportunities for analyzing and quantifying the visual components in the books. The production technology for illustrations in books in the 19th century was characterized by a shift from wood or copper engraving to lithography. We report classification experiments which intend to classify images based on the production technology. For a classification task that is also difficult for humans, the classification quality reaches only around 70%. We analyze some further error sources and identify reasons for the low performance.